Given the high cost of large language model (LLM) training from scratch, safeguarding LLM intellectual property (IP) becomes increasingly crucial. As the standard paradigm for IP ownership verification, LLM fingerprinting thus plays a vital role in addressing this challenge. Existing LLM fingerprinting methods verify ownership by extracting or injecting model-specific features. However, they overlook potential attacks during the verification process, leaving them ineffective when the model thief fully controls the LLM's inference process. In such settings, attackers may share prompt-response pairs to enable fingerprint unlearning, or manipulate outputs to evade exact-match verification. We propose iSeal, the first fingerprinting method designed for reliable verification when the model thief controls the suspected LLM in an end-to-end manner. It injects unique features into both the model and an external module, reinforced by an error-correction mechanism and a similarity-based verification strategy. These components are resistant to verification-time attacks, including collusion-based fingerprint unlearning and response manipulation, backed by both theoretical analysis and empirical results. iSeal achieves 100% Fingerprint Success Rate (FSR) on 12 LLMs against more than 10 attacks, while baselines fail under unlearning and response manipulations.

Backdoor attacks in data outsourcing settings pose severe risks to deep neural networks. Specifically, adversaries can manipulate externally sourced training data to implant hidden behaviors in target models (e.g., incorrect predictions on triggered samples). Existing defenses are either pre-processing or post-processing. Since the two approaches are orthogonal and either one can independently strengthen real-world defenses, we focus on the latter in this paper. Yet current post-processing defenses face one or more of the following issues: overemphasis on output logits while overlooking rich information in intermediate layers, injection of uncertain new triggers while requiring alignment with the original triggers, and underuse of poisoned model representations. To overcome the aforementioned limitations, we propose ATClean, an adaptive post-processing defense based on feature collisions in latent space. Specifically, it leverages all layers rather than only output logits to capture backdoor-affected regions using an adaptive loss function, relaxes the need for exact trigger reconstruction by generating adversarial samples that only enforce feature collisions with a theoretical guarantee, and fully exploits poisoned representations with feature-collision-based fine-tuning. Experiments across benchmark datasets, multiple architectures, and seven representative attacks show that ATClean achieves state-of-the-art defense effectiveness with the lowest drop on clean data, including about a 20% improvement in DER, which measures the accuracy-defense trade-off.

The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can lead to substantial losses for the game industry. Existing anti-cheat solutions have limitations, such as client-side hardware constraints, security risks, server-side unreliable methods, and both-sides suffer from a lack of comprehensive real-world datasets. To address these limitations, the paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO. HAWK utilizes machine learning techniques to mimic human experts’ identification process, leverages novel multi-view features, and is equipped with a well-defined workflow. HAWK is evaluated with the first large and real-world datasets containing multiple cheat types and cheating sophistication, and it exhibits promising efficiency and acceptable overheads, shorter ban times, higher recall and similar false positive rate compared to the in-use anti-cheat, and the ability to capture cheaters who evaded official inspections.

A cloud-native application is kind of software built to fully leverage a cloud computing environment, rather than being a traditional application adapted for the cloud. The goal is to create agile, resilient, and scalable applications that can be deployed and managed efficiently in any cloud environment. It is a layered approach with four layers (4C) that need to be secured, including the code, the container, the cluster, and the cloud. Due to its importance, security is a priority for such applications. To enhance the security, it needs to be built into the application and infrastructure from the ground up, rather than added on as an afterthought. This requires a combination of automated security controls. Motivated by this requirement, in this work, we aim to build a secure cloud-native infrastructure via cloud-native technologies such as infrastructure as code (IaC) and DevOps, and demonstrate its performance by deploying it on a public cloud provider AWS (Amazon Web Services).

The rapid growth of the Internet of Things (IoT) has introduced significant security challenges, particularly in detecting intrusions within complex IoT networks. This paper presents TS-Net, a robust dual-channel model that combines temporal and spatial feature learning to enhance IoT intrusion detection. By partitioning network traffic into temporal and spatial features, TS-Net processes them through separate channels. The temporal channel utilizes Bidirectional Gated Recurrent Units (BiGRU) paired with a self-attention mechanism to capture dynamic sequential dependencies, while the spatial channel employs multi-scale dilated convolutions to extract patterns from varying spatial perspectives. These two channels are then fused to improve the model accuracy in detecting anomalous traffic. Experimental results on three publicly available datasets demonstrate that TS-Net outperforms existing intrusion detection models, achieving higher precision, recall, and F1-scores, demonstrating its effectiveness in addressing the unique security needs of IoT networks.

Multipath cooperation technology alleviates transmission pressure by leveraging path diversity. However, in next-generation service-oriented environments with computation-intensive services, the limited computing capability of transmission paths can degrade end-to-end service quality, even when bandwidth is sufficient. This issue becomes more pronounced in dynamic mobile scenarios, where fluctuating link status and computational resources introduce new challenges in path selection. To address these challenges, we propose a novel path selection approach that jointly considers both network and computation constraints for computation-intensive services. First, we construct a computation-integrated multipath transmission framework to support real-time monitoring of both link-level computing capabilities and network conditions. Second, we introduce a packet structure embedding device identifiers and computing capability, enabling adaptive scheduling. Finally, we develop a computing capability-constrained delay-minimizing packet scheduler (C2-DMPS) to balance bandwidth and computational load, ensuring low-latency transmission for emerging service demands. The results demonstrate the critical role of computational capacity in maintaining service performance, especially under volatile network conditions, highlighting potential risks to service continuity in next-generation environments.

The global healthcare landscape has witnessed accelerated adoption of digital transformation initiatives, with electronic health records (EHRs) emerging as the cornerstone technology for modern medical data management. While EHR systems effectively consolidate patient information and improve clinical continuity, their predominant centralised architectures create data silos that impede cross-institutional interoperability and patient sovereignty over personal health data. In this paper, we propose a blockchain-enabled EHR sharing framework that integrates attribute-based encryption with hierarchical role-based access control (RBAC) mechanisms. Our dual-protection architecture combines 1) an attribute-oriented privacy preservation scheme that ensures data immutability and fine-grained access through cryptographic proofs, with 2) a multi-level role hierarchy system that enables efficient permission management among healthcare stakeholders. By establishing dynamic mappings between user attributes and institutional roles, we achieve secure yet flexible authorization processes without compromising system performance. Experimental evaluations demonstrate superior performance metrics in cryptographic operations and access verification latency, validating the practicality of the framework for real-world EHR exchange scenarios.

Machine learning is gradually transforming medical image segmentation. However, its accuracy often relies on large-scale medical datasets, while centralized data collection raises serious privacy concerns. To address this issue, federated learning (FL) enables collaborative model training without sharing raw data, thus effectively protecting patient privacy. Despite this advantage, commonly used segmentation models, such as U-Net and its variants, typically have large parameter sizes, making them inefficient for local training on FL clients. To overcome this challenge, we propose FedU-KAN, a framework built upon the lightweight U-KAN architecture, tailored for federated medical image segmentation tasks. Moreover, we design an adaptive differential privacy mechanism that dynamically adjusts gradient clipping based on feature importance. This approach helps preserve anatomical details while reducing the risk of privacy leakage. We evaluate FedU-KAN on the CVC-ClinicDB and Kvasir-SEG datasets, where it achieves IoU scores of 87.09% and 83.38%, respectively—outperforming standard FL baselines. These results demonstrate that FedU-KAN can effectively balance privacy protection and model performance in real-world medical segmentation scenarios.

The proliferation of electric vehicles (EVs) has spurred a growing demand for efficient battery exchange and charging services, making the battery swapping-charging system (BSCS) an attractive solution.The various subsystems of the BSCS exchange data in real-time through wireless communication. However, due to the openness of wireless communication, data can be easily intercepted and tampered with during transmission, which may lead to the leakage of sensitive information. To address this, we introduce a privacy-preserving distributed optimization algorithm, leveraging homomorphic encryption and multi-party secure computing in the BSCS context. Initially, we formulate the operation management problem of BSCS problem as a constrained mixed integer programming (MIP) and employ the alternating direction method of multipliers (ADMM) for optimal resolution. Subsequently, we integrate ADMM with the Paillier cryptosystem for privacy protection. Empirical validation substantiates the algorithm security and convergence, ensuring that adversaries cannot deduce private information. Notably, the proposed algorithm yields a solution closely resembling the centralized solution, with a superior convergence rate compared to alternative methods.

Although federated learning offers a certain degree of privacy by aggregating user gradients instead of raw data, it remains vulnerable to various attacks, such as model poisoning. Existing defense mechanisms often address poisoning threats at the cost of exposing gradient information, which can lead to privacy risks such as member inference attacks. While techniques like cryptography or differential privacy can be employed to mitigate these risks, they often come with significant efficiency trade-offs. At the same time, a non-IID heterogeneous environment is also a big challenge. To address these challenges holistically, this paper proposes a dual-layer detection scheme (EDDFL). It combines norm-based filtering and isolation forest detection to effectively filter out malicious gradients, thereby preserving model accuracy even in adversarial environments. Furthermore, we incorporate a gradient quantization method that not only protects gradient privacy but also improves communication efficiency. Compared with existing approaches, the proposed method effectively addresses the challenges of model poisoning, gradient leakage, and data heterogeneity under non-IID settings. Experimental results demonstrate that our scheme significantly reduces both computational and communication overhead while maintaining privacy guarantees.

Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning and retention. It motivates this paper to explore enhanced unlearning schemes that can mitigate this trade-off. Specifically, we propose Gradient Rectified Unlearning (GRU), an improved framework that regulates the directions of gradient updates during the unlearning procedure such that their side impacts on other, unrelated responses can be minimized. GRU is easy and general to implement, demonstrating practical effectiveness across a variety of well-established unlearning benchmarks.

The advent of unmanned aerial vehicle (UAV) networks, renowned for their expansive coverage capabilities and heightened adaptability, presents a promising landscape for bolstering the efficacy of Internet of Things (IoT) data transmissions. Nevertheless, the integration of UAVs into IoT ecosystems introduces a spectrum of security challenges, notably data tampering, man-in-the-middle (MitM) attacks, and eavesdropping, which threaten the integrity and confidentiality of transmitted information. Since covert transmission has the characteristics of strong concealment and difficult detection, UAV networks based on information hiding become a new paradigm for solving these security problems. This article proposes an information hiding algorithm based on super-resolution enhancement and significant region detection (IH-SESD). This algorithm incorporates the super-resolution enhanced SRCNN and the U2Net salient region detection technologies. Comprehensive performance analysis and comparisons with existing methods demonstrate the superiority of the proposed IH-SESD algorithm.

Vehicular social networks (VSNs) play a crucial role in intelligent transportation systems, offering high-quality data management services that enhance various aspects of daily life. Due to their convenience, VSN systems, equipped with advanced data search and sharing capabilities, are increasingly integrated into modern vehicles. While earlier VSNs focused on securing data communication between users, the transmission of sensitive vehicle and traffic data, like road conditions and vehicle trajectories, has raised privacy concerns and the risk of data leakage, which could harm vehicle owners’ interests. Historically, these systems focused primarily on securing data communication between VSN users. However, the transmission of sensitive vehicle and traffic data, such as road conditions and vehicle trajectory information, has raised concerns about data privacy and the potential risks of data leakage, which could compromise the interests of vehicle owners. To address these challenges, we propose a novel group-grained data search and sharing scheme for VSN systems. Unlike traditional attribute-based encryption methods used in data management, our approach introduces a group-grained model that enables fine-grained control over search rights and data-sharing isolation, ensuring enhanced data privacy. Additionally, to reduce the computational burden on these Internet of Thing (IoT) devices, our scheme ensures constant-sized keyword index generation, data index generation, trapdoor creation, and decryption processes. We evaluate the efficiency of our construction and compare it with similar constructions. The results demonstrate that our construction is well suited for resource-constrained IoT devices in VSN systems.

The adoption of container-based cloud computing services has been prevalent, especially with the introduction of Kubernetes, which enables the automated deployment, scaling, and administration of applications in containers, hence boosting the popularity of containers. As a result, researchers have placed greater emphasis on container runtime security, notably investigating the efficacy of traditional techniques such as Capabilities, Seccomp, and Linux security modules in guaranteeing container security. However, due to the limitations imposed by the container environment, the results have been unsatisfactory. In addition, eBPF-based solutions face the problem of being unable to quickly load policies and affect real-time operations when faced with newer kernel vulnerabilities. This paper investigates the limitations of existing container security mechanisms. Additionally, it examines the specific constraints of these mechanisms in Kubernetes environments. The paper classifies container monitoring and obligatory access control into three distinct categories: system call access control, LSM hook access control, and kernel function access control. Therefore, we propose a technique for regulating container access with a variety of granularity levels. This technique is executed using eBPF and is tightly integrated with Kubernetes to collect relevant meta-information. In addition, we suggest implementing a consolidated routing method and employing function tail call chaining to overcome the limitation of eBPF in enforcing mandatory access control for containers. Lastly, we conducted a series of experiment to verify the effectiveness of the system's security using CVE-2022-0492 and to benchmark the system that had BPFGuard enabled. The results indicate that the average performance loss increased merely by 2.16%, demonstrating that there are no adverse effects on the container services. This suggests that greater security can be achieved at a minimal cost.

Edge computing, an advanced extension of cloud computing, provides superior computational capabilities and lowlatency processing at the network edge, facilitating its availability for real-time data analysis in resource-limited settings. When applied to the analysis of teaching methodologies, edge computing enables the seamless integration of vocal and physical cues, facilitating collaborative, dynamic, and real-time evaluations of teaching quality. However, the inherent complexity of human perception and multimodal interactions impose great challenges to the analysis of these aspects in Artificial Intelligence of Things (AIoT). This paper introduces an innovative mathematical model and a measurement index specifically designed to assess changes in voice-body coordination over time. To achieve this, we propose a cloud-enabled enhanced Bi-Linear Attention Network incorporating entropy and Fourier transforms (BAN-E-FT), which leverages both temporal and frequencydomain features. Specifically, by harnessing the computational and storage capabilities of edge computing, BAN-E-FT facilitates distributed training, expedites large-scale data processing, and enhances model scalability, where entropy measures and Fourier transforms capture modality dynamics, enhancing BAN's fusion capabilities. Moreover, a conditional domain adversarial network is embedded to address regional teaching variations, improving model generalizability. We also verify the robustness of BAN-EFT with accuracy and convergence through convex optimization analysis. Experiments on the eNTERFACE'05 dataset demonstrate 81% accuracy in assessing teaching adaptability, while real-world test at Guizhou University confirms 78% accuracy when using BAN-E-FT, matching human expert assessments.

Machine learning has the potential to revolutionize medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will compromise patient privacy. Federated learning is a promising technique that protects patient privacy and improves the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving federal learning scheme via hybrid differential privacy for medical image classification (FHDM). Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm combines Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 9.60% compared to previous differential privacy schemes with the same medical image dataset and privacy budget.

Self-Sovereign Identity (SSI) shifts identity management authority from central institutions to users, enhancing privacy protections. However, malicious identity providers may collude with users to issue credentials that pass verification but contain false information. While multi-signature schemes enable joint credential issuance by multiple identity providers to mitigate this risk, they result in a linear increase in the size of signatures within credentials and verification keys as the number of identity providers grows, leading to substantial storage overhead for both users and verifiers. Furthermore, malicious verifiers may leak users' credentials to third parties or unlawfully duplicate them, causing users to lose control over the distribution of their credentials. To address these challenges, we propose the Accountable Many-to-One Signature Scheme with Short Verification Key (ASVK-MOSS), a multi-signature scheme with designated verifiers and accountability that ensures fixed sizes for both the verification key and the signature. We prove the security of the proposed ASVK-MOSS under the random oracle model. Building on ASVK-MOSS, we design a novel SSI system, namedMO-SSI, in which multiple identity providers jointly sign the user's personal information to generate a credential with fixed sizes. Service providers only need to store a minimal, fixed-size verification key to validate the credentials from any set of identity providers. Additionally, the designated verifier and accountability mitigate risks posed by malicious service providers and identity providers within MO-SSI. The theoretical analysis and experimental results demonstrate its effectiveness and feasibility.

In IoT-enabled smart home scenarios, heterogeneous communication devices such as Bluetooth (BT) and Wi-Fi are widely used in applications such as home automation, remote monitoring, and intelligent device interconnection. However, in such a multi-device coexistence environment, efficiently allocating limited time-frequency resources to mitigate communication interference and enhance system performance has become a critical challenge. To address these issues, this article proposes a comprehensive solution that integrates master selection, resource allocation, and task scheduling to optimize resource utilization and service quality in smart home IoT systems. For device management, we propose a hierarchical entropy weight method (HEWM), considering factors such as device parameters, sensing capabilities, communication performance, and device interoperability. This method ensures efficient and stable selection of the primary device, optimizing network topology and communication efficiency. For resource allocation, we introduce a proximal policy optimization (PPO) algorithm that dynamically adjusts time-frequency resource allocation based on the varying device usage, network load, and communication condition. This adaptive strategy reduces interference between devices and improves system throughput. For task scheduling, we develop a task urgency-based queueing (TUQ) mechanism that prioritizes tasks based on urgency. A task preemption mechanism ensures that high-urgency tasks are processed with minimal delay, enhancing scheduling efficiency and service responsiveness. Simulation results show that the proposed approach significantly outperforms traditional methods in smart home IoT scenarios, achieving higher primary device scores, a 3%–22% improvement in system throughput, and a 5%–36% reduction in task delay.

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon.

Machine learning is widely used in medical image classification tasks. However, medical images often exhibit uneven distribution and high sensitivity to noise. A feasible solution involves using federated learning (FL) with differential privacy (DP), a distributed training method that protects patient privacy. In this work, many studies have proposed improved solutions for localized differential-private federated learning (DP-FL) frameworks. However, these studies are isolated, which would be detrimental to privacy practitioners in designing and using the algorithms. To provide a comprehensive analysis of these algorithms, we propose DPFedSAM-Meas, as a framework for comprehensive utility analysis of DP-FL. In this framework, we employed the state-of-the-art federated learning framework FedSAM. Moreover, we categorize DP algorithms into Laplace DP and Gaussian DP by the underlying DP mechanisms, and into Gradient DP and Parameter DP by the DP position in FL train. DPFedSAM-Meas allows a comparative analysis of these four DP techniques, measuring their model utility, privacy leakage, and overhead when FL uses different network structures. Finally, we evaluate DPFedSAM-Meas on datasets of Pneumonia, Blood, and Path, aiming to investigate the performance of different DP techniques on mainstream deep learning algorithms, including Convolutional Neural Networks (CNN) and Vision Transformers (ViT).

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model’s output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary’s flexibility to alter the model’s output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack’s effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting.

Federated learning, while advancing data privacy, faces risks of sensitive information leakage through parameter updates, making it susceptible to inference and data reconstruction attacks. Fraudulent behaviors by central servers or clients can undermine the integrity of model training, thereby reducing accuracy and affecting decision-making quality. This paper introduces a bidirectional, privacy-preserving verifiable federated learning framework(BPVFL) built on homomorphic encryption and a novel three-party zero-knowledge protocol. This framework guarantees the integrity of server aggregation and the credibility of the information uploaded by clients. Experimental results demonstrate that BPVFL effectively protects client privacy, prevents fraud by servers and certain clients, and efficiently handles numerous client disconnections with minimal overhead.

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security.

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients’ private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients’ privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients’ privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget.

At present, multipath transmission realized by multi-interface devices and bandwidth aggregation technology meets user demand for high-bandwidth communication in 6G wireless networks. However, multipath transmission systems face the threat of single-point failure by multi-interface servers themselves. Existing solutions for such failure are not suitable for multipath transmission scenarios, and this seriously limits the ability to bandwidth aggregation and reduces the reliability and invulnerability of the multipath transmission system in 6G wireless networks. In this paper, we propose a novel Intelligent Device Disaster Recovery (Multi-ID2R) mechanism to solve the single-point failure in multipath and aggregated environments for the first time. In particular, we establish the Multi-Dimensional Parameter Joint Analysis model (MDPJA) and propose an algorithm for judging the running state of multi-interface devices. The algorithm takes into account the different network parameters of the paths, including delay, packet loss rate, and throughput. Moreover, an intelligent switching mechanism based on service quality is designed. Multi-ID2R comprehensively considers the characteristics of the business and the current parameters of multipath networks to determine the moment of switching to flexibly adjust switching strategies. Finally, we deploy the mechanism on multi-interface servers in actual networks. Experiments demonstrate that, compared with Virtual Router Redundancy Protocol, Gateway Load Balancing Protocol, and Hot Standby Router Protocol, Multi-ID2R effectively improves the reliability and invulnerability of multi-interface server in 6G wireless networks.

The diversity of the Internet of Things (IoT) poses challenges to privacy protection, especially in the field of digital forensics. How to ensure that only the private information of the suspect is provided, and not the irrelevant information of other users is disclosed is crucial, especially when obtaining evidence in the complex IoT environment. To the best of our knowledge, there are few studies on protecting the privacy of irrelevant users in the IoT forensics. However, it is very important to ensure that the evidence does not violate the privacy of other users when collecting evidence, because it directly determines whether the evidence is legal and whether it can be admissible in court. In this paper, a new method based on data provenance graph is designed to solve the privacy protection problem of IoT forensics. The key idea of this method is to protect privacy by dividing multi-user information and protecting it from an encryption perspective. The method consists of three main phrases: distinguishing different users' data provenance graphs using traversal search, node abstraction, and hiding techniques, utilizing pseudo-random dual-key negotiation methods tailored for the scenario to enhance privacy protection for unrelated users, and employing identity authentication technology to facilitate better investigation and extraction of data provenance graph information of criminal accomplices in specific scenarios. Example proves that this method has practical significance and promising application prospects in protecting the privacy of unrelated users in IoT forensics while ensuring evidence accessibility in special criminal scenarios.

As smart home IoT systems gain popularity, they inevitably become targets for security risks and concerns. Among various cyber-attacks targeting these systems, the fake event attack poses significant issues due to its ability to manipulate secure devices through automation rules. In response to this threat, we propose TrustEvent - a system designed to offer end-to-end event signature verification. By integrating TrustEvent with existing home automation platforms, event authenticity is verified against signatures generated from edge devices before these events trigger automation rule execution. Notably, we have developed a signature proxy module, enhancing our system's compatibility across various platform scenarios. We have implemented a TrustEvent prototype in conjunction with existing commercial smart home IoT platforms, evaluating its overhead in the process. Our experimentation demonstrates that our system only marginally increases the automation execution latency, by an average of 3.74 seconds, representing a acceptable compromise for enhanced security.

Digital forensics is a security research field that has evolved with the advancement of digital technologies, such as computer and network technology. With the emergence of complex forensic environments, such as those found in the IoT and cloud computing, investigators are required to possess higher technical capabilities and knowledge. Completing digital forensics tasks more effectively has become a challenge. Therefore, this paper proposes a blockchain-based collaborative crowdsourcing platform for digital forensics. Through collaborating on the crowdsourcing platform, professional investigators can enhance the supervision of the digital forensics process, reduce the difficulty of digital forensics tasks, and improve their quality. In addition, we introduce blockchain technology to maintain the credibility of the investigation process. We have developed a smart contract and conducted experiments based on Ethereum. The experimental results show that our solution is feasible.

In this paper, we design and implement an Intrusion Detection System (IDS) within the 5G core network, which is capable of inspecting both IP and non-IP data flows. By leveraging the Access and Mobility Management Function (AMF) Network Function (NF) communication service, our IDS can analyze all Cellular Internet of Things (CIoT) data traffic flowing across both the User and Control Planes (UP and CP), enabling the detection of malicious activities originating from or targeting IoT networks. Our proposal is aligned with the 3GPP Release 17 (R17) standard and makes use of predefined functionalities to ensure compliance. Our proposal is non-intrusive and does not interfere with the core network’s usual processes based on existing Service Based Interfaces (SBI). Additionally, we demonstrate that the classification of a data packet as malicious or benign is context-dependent using AI/ML Transformer Encoder architectures. We implement and integrate our proposed 5G-CIoT IDS as a Network Function inside the 5G Amarisoft platform for extensive experimentation. To evaluate the models’ performance, we train our models with different categories of safe and malicious generated traffic and apply them to an emulated realistic scenario. We obtained a very promising result.

The encryption of traffic data offers a means of protecting the security of data and the private information of the public. However, this same technology also presents a potential avenue for attackers to conceal malicious activities. Attackers hide malicious behaviour in encrypted traffic data to bypass detection by firewalls or early intrusion detection systems (IDS). In order to cope with malicious encrypted traffic, traffic attack detection is classified into active and passive detection depending on the way it is handled. Active detection is mainly based on searchable traffic detection and traffic plaintext data parsing. Measures such as analysing controllable transmission protocols and trusted execution environments are used to ensure both attack detection efficiency and privacy security. Passive detection focuses on feature construction of encrypted traffic. The characterisation of traffic data from multiple perspectives, including channel and context, is achieved through the utilisation of machine learning or deep learning models, thereby facilitating the generation of accurate prediction outcomes. This paper offers an overview of existing detection methods from two distinct vantage points: active attack detection and passive attack detection. Finally, the paper presents a summary of the strengths and weaknesses of existing methods and suggests potential avenues for future research.

Along with the increasing popularity of smart home IoT devices, more users are turning to smart home automation platforms to control and automate their IoT devices. However, IoT automation is vulnerable to spoofed event attacks. Given that IoT devices are intricately linked with the physical environment and operate autonomously, event-based attacks can pose serious safety and security challenges. Our observations show that many IoT events are accompanied by visual modifications in objects such as shape alterations (for example, contact sensor events correspond with door movement) or changes in color/brightness (for example, a functioning microwave oven with the internal light switched on). These alterations can be detected by the commonly deployed smart cameras, providing a visually rich but challenging to manipulate channel for verifying IoT events. We introduce IoTSentry, the first system of its kind to extract high-level semantic information from streaming video data and pixels for IoT event verification. We have designed a Siamese deep neural network to identify variations in the appearance of IoT devices and interior objects. These are used as the yardstick for verifying IoT events received at IoT automation platforms. Upon assessing IoTSentry with 21 IoT devices (8 types), the results demonstrate that IoTSentry can be trained within 120 seconds, yielding an accuracy rate of over 96.7% in recognizing device states. We have deployed the 21 IoT devices and IoTSentry on two real-world smart home test sites. Over the course of our one-week evaluation, IoTSentry consistently achieved an average detection rate of 99.24% in identifying attack instances. Moreover, it triggered no more than 2 false alarms per day on each test site.

The increasing demand for intelligent management in modern power systems has emphasized the importance of smart grids, which facilitate real-time analysis and management through data aggregation. Fog computing provides efficient data processing and low-latency transmission for data aggregation. However, fog-assisted smart grids still face significant challenges, including privacy leakage, calculation limitations, and system stability issues. In response to these obstacles, we propose a robust and privacy-enhanced multidimensional data aggregation (RPMDA) scheme. Specifically, the Chinese Remainder Theorem is used to improve the efficiency of processing multidimensional data, combined with an innovative double-masking method to cope with secure data aggregation. For the purpose of reliable authentication, a conditional anonymous certificateless signature algorithm is designed in RPMDA, where the pseudonym generation mechanism ensures the conditional anonymity of smart meters. Besides, our scheme incorporates robustness, ensuring that the aggregated results remain unaffected even if smart meters malfunction. Compared to the existing solutions, RPMDA shows superior performance while meeting security requirements.

Nowadays, we are living in an open network environment with varieties of smart devices, which makes individual privacy face unprecedented threats. For one thing, a plenty of sensitive information may be gathered without the owner’s knowledge. For the other, the Internet of Things (IoT)-based services and various intelligent applications require a large amount of perceptual data. And in practice, these data are usually encrypted and stored in storage providers like cloud for security and cost saving. To fully harness the productivity value of data and protect privacy, ciphertext-policy attribute-based encryption (CP-ABE) is widely used. Nevertheless, most existing CP-ABE schemes cannot work well for IoT because of the heavy overhead and the open and distributed environment. To lower the cost, a lightweight CP-ABE scheme without pairing is proposed and proved in the set-selective mode. Both the theoretical analysis and experiments show its advantages in computation, communication, and storage overhead. For flexible access control in IoT, we attempt to employ the masked authenticated message (MAM) mechanism of the IOTA to manage authorization for our CP-ABE scheme. Comparisons with similar schemes show that it can overcome the low throughput and monetary cost in other distributed ledger-based access control schemes.

Spatial crowdsourcing (SC) has become a fundamental and emerging technology in Metaverse, facilitating the creation of immersive experiences through location-based services. In these systems, a central SC server leverages SC workers who physically travel to task locations to gather spatiotemporal environment data. However, conventional SC systems face two significant challenges: (1) the SC server, functioning as a centralized authority, can sometimes be unreliable, either due to intentional or unintentional misconduct, (2) to ensure efficient task assignment and validation, the location privacy of tasks and workers is openly accessible. In this study, we formally define location privacy preserved proof generation and verification problem (LP-PGVP) within an SC task matching scenario, with the aim to the above two challenges. Our proposed solution is a blockchain-based SC system (BlockSC), which provides a decentralized platform for task requesters and workers in the Metaverse context through calling smart contracts. We also introduce a ciphertext-based task matching scheme where task location access is granted only to eligible workers executing a task, benefiting from the design of geographic coordinate transformation and bilinear mapping methodology. To further demonstrate the task matching scheme’s operation and impact, we present an easy-to-understand case study. Our evaluation findings confirm that the proposed system effectively maintains location privacy for both SC workers and task requesters, without a considerable sacrifice in task matching efficiency.

Multipath transmission is a critical enabling technology to enhance QoE for edge users. The packet scheduler plays an irreplaceable role in overcoming heterogeneity and dynamicity in multipath transmission. However, current schedulers depend on an inaccurate delay estimation and lack systematic traffic intensity awareness, performing poorly in wireless heterogeneous networks (HetNets). In this paper, we propose a novel traffic-aware two-level packet scheduler (TA2LS) to address the problem and improve aggregated bandwidth while trading off delay. In particular, we design a multipath transmission state machine (MTSM) to perceive link traffic intensity. MTSM replaces network prediction algorithms by identifying the contribution of each link in multipath transmission in a cost-effective way. Further, we propose a scheduling mechanism based on a two-level optimal-path evaluation method (2LOSM) to adjust the packet scheduling policy adaptively. 2LOSM increases the priority of links with low traffic intensity during scheduling, improving aggregated bandwidth performance and reducing end-to-end delay. We have built a real-world 4G/5G/WiFi testbed and deployed 47 dynamic scenarios to evaluate TA2LS and other five schedulers. In 4G/5G/WiFi scenarios, TA2LS improves aggregated bandwidth by 10.32%–48.27% compared to the second-best scheduler and reduces end-to-end delay by 5.04%–39.98% under the premise of fewer or equivalent overheads.

In the age of data science and connected devices of all kinds, users have gained a lot of convenience. However, the massive data generated by users from the communication network is faced with security problems such as intrusion, tampering, hijacking, etc. According to the properties of covert transmission, the network with data covert embedding becomes an effective means to solve these challenges. In this paper, we propose a four-dimensional space-based data multi-embedding mechanism to protect data in the network. Specifically, a feature extractor is first used to extract vectors for embedding. Next, a Schmitt-based four-dimensional space is constructed, two of which are used to embed robust data to hide confidential information. The other two dimensions are used to embed fragile data to detect whether the signal has been tampered with. According to the experimental comparison with other methods, it shows that the method proposed in this paper achieves satisfactory performance.

Federated learning (FL) is a prominent paradigm of 6G edge intelligence (EI), which mitigates privacy breaches and high communication pressure caused by conventional centralized model training in the artificial intelligence of things (AIoT). The execution of multimodal federated perception (MFP) services comprises three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately competitive on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is vital to the MFP networks. To address the above issues, this article explores service-oriented resource management with integrated sensing, communication, and computing (ISCC). Specifically, employing the incentive mechanism of the MFP service market, the resources management problem is defined as a social welfare maximization problem, where the concept of “expanding resources” and “reducing costs” is used to enhance learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms.

As one of the indoor communication technologies, visible light communication (VLC) has drawn great attention for its advantages such as ultra-wide unlicensed spectrum, power saving and low complexity. The nature of the visible light propagation is an open channel, which is vulnerable to wiretapping. This paper investigates a secure VLC mechanism enabled by multiple light fixtures acting as friendly jammers. The goal of the friendly jammers is to diminish the capability of the eavesdropper to infer the undisclosed information, on the premise of causing minimal impact on the legitimate receiver. For this reason, an algorithm based on reinforcement learning is proposed to dynamically optimize the friendly jamming policy in realistic nonstationary environments. In order to resolve the difficult problem of the dimensional curse and to effectively represent the continuous state and action spaces, an algorithm based on deep reinforcement learning is devised, which utilizes deep convolutional neural networks to accelerate the convergence rate of the learning process. A differentiable neural dictionary is introduced to make full use of the experiences in similar anti-eavesdropping scenarios to improve the learning capability. Simulation results demonstrate that, the proposed schemes can achieve a higher secrecy rate and a lower bit error rate than some state-of-the-art schemes.

Massive random access of devices brings great challenge to the management of radio access networks. Most of the time, the access requests in the network is sporadic. Exploiting the bursting nature, sparse active user detection (SAUD) is an efficient enabler towards efficient active user detection. However, the sparsity might be deteriorated in case of high concurrent request periods. To dynamically coordinate the access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring (ACB) technique is proposed, where the control policy is determined through continuous interaction between the RL agent and the environment. The proposed RL agent can be deployed at the next generation node base (gNB), supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An Actor-Critic framework is formulated to incorporate the strategy-learning modules into the intelligent control agent. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

The dramatically increased wireless communication needs make non-renewable spectrum resources extremely scarce and costly. Consortium blockchain realizes trusted spectrum sharing among untrusted spectrum owners. Yet, most existing studies ignore spectrum sharing among blockchains, which greatly reduce spectrum utilization. In the paper, we focus on cross-chain spectrum sharing. We propose a Fine-grained Cross-chain Spectrum Sharing mechanism based on Oracle (FCSSO) to realize trusted and efficient cross-chain spectrum transactions. To guarantee benefits of spectrum owners, we design a fine-grained time partition method to decide spectrum renting time in transactions. The method reduces the waste of owners' available spectrum time caused by spectrum handoff. Extensive simulation proves the positive impact of the proposed fine-grained time partition method, and FCSSO outperforms two representative cross-chain mechanisms from two aspects: spectrum owners' benefits and spectrum utilization.

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.

In this paper, our objective is to design, develop and deploy a novel 5G-IoT IDS as a 5G core network function compliant with 3GPP R17. 5G-IoT IDS provides protection against malicious behaviors targeting IoT networks. To satisfy the 3GPP standard, our proposal respects the design architecture of the 5G system and only uses functionalities defined by the 3GPP technical specifications. Using Open5GS emulating the 5G core network, we implemented and integrated the 5G-IoT IDS as an NF to inspect IoT MQTT traffic on the user plane with common ML algorithms to demonstrate feasibility and effectiveness of our proposal. We explored a different way of handling MQTT packets, delving deeper into the structure of the packet. Based on extensive emulations, we compared our results with analogous studies focused on the MQTT protocol, and it revealed that our emulations exhibit strong performance, which aligns with those highlighted in the related studies, when up against a variant attack of the same flood-based principle. We believe our method of packet handling demonstrates a more comprehensive consideration of MQTT packet characteristics.

With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier bound to each individual, transmitting them directly to the cloud may bring about serious privacy disclosure risks. To guarantee users' biometric features, there are many solutions have been proposed. However, most of them neglect to protect identity security. In light of the challenges, this paper proposes a strong privacy-preserving fingerprint authentication via clustering. Besides safeguarding fingerprint features, the scheme also blurs the identities of users to enable anonymity of identity. Meanwhile, the authentication efficiency of the proposed scheme is improved by vector processing of fingerprints and fast retrieval of clustered identities. Furthermore, a dual-server matching architecture effectively reduces the communication overhead of the service provider. The security analysis and experimental results indicate that the proposed scheme provides strong privacy preservation while maintaining high efficiency.

The multipath transmission structure decouples network services from a single transmission carrier, which has great potential for shaping a more secure and efficient 6G network. Existing multipath transmission schemes face challenges such as network heterogeneity, perception lag, and additional scheduling delay, which limits their ability to improve bandwidth aggregation capacity and information security. To address these issues, we propose the Sequential Reinforcement Evolution (SRE) scheme, which utilizes deep reinforcement learning to predict the value of future scheduling actions based on past network states. The SRE scheme regards improving bandwidth aggregation capacity and anti-eavesdropping ability as optimization goals, and designs a semi-symmetric attention recurrent neural network (SARNN) to better mine the sequential nature of the scheduling process. The SRE scheme utilizes approximately 500 million real network data points to pre-train the SARNN model, and performs cycle optimization during the actual deployment process. Experimental results show that SRE significantly outperforms state-of-the-art scheduling schemes with a 32% increase in bandwidth aggregation and a 117% increase in traffic security dispersion with minimal impact on latency.

In recent years, a novel mobile computing paradigm has been evolving rapidly, with a host app allowing users to install and run mini-apps inside the app itself. However, the current classification mechanism of mini-apps is blurry and coarse-grained, making users unable to clearly understand mini-app functions, which can result in a series of privacy issues. In this study, an automatic convolutional neural network (CNN)-based classification approach is proposed for Wechatmini-apps. The proposed method integrates the static and dynamic features of WeChat mini-apps to achieve precise classification. Our approach was evaluated in a real-world testbed and the results showed that it can effectively classify Wechatmini-apps into proper categories, helping users better understand the functions of WeChat mini-apps while reducing user privacy violations.

Membership inference attack (MIA) has been proved to pose a serious threat to federated learning (FL). However, most of the existing membership inference attacks against FL rely on the specific attack models built from the target model behaviors, which make the attacks costly and complicated. In addition, directly adopting the inference attacks that are originally designed for machine learning models into the federated scenarios can lead to poor performance. We propose GBMIA, an attack model-free membership inference method based on gradient. We take full advantage of the federated learning process by observing the target model's behaviors after gradient ascent tuning. And we combine prediction correctness and the gradient norm-based metric for membership inference. The proposed GBMIA can be conducted by both global and local attackers. We conduct experimental evaluations on three real-world datasets to demonstrate that GBMIA can achieve a high attack accuracy. We further apply the arbitration mechanism to increase the effectiveness of GBMIA which can lead to an attack accuracy close to 1 on all three datasets. We also conduct experiments to substantiate that clients going offline and the overlap of clients' training sets have great effect on the membership leakage in FL.

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.

Multipath transmission is an important solution in 6G wireless networks to improve communication resource convergence among heterogeneous networks. The appearance of out-of-order (OFO) packets is a problem in multipath transmission, especially in heterogeneous wireless networks, where network states fluctuate frequently. Existing schedulers attempt to solve the OFO problem by designing an “in-order” strategy. However, these “in-order” strategies lose their roles in jittered networks, resulting in poor throughput performance. In this study, we propose a novel packet scheduling strategy, called a multipath-state machine (MSM) scheduler. MSM prioritizes improving throughput utilization over ensuring packets arrive in order. Based on whether links are fully utilized, MSM divides link utilization states into 5 phases, including establishment, idle, busy, congestion, and risk. MSM prefers underutilized links. Furthermore, MSM chooses the one with the shortest transmission delay when multiple paths are in the same utilization state. Experiments show that MSM provides a more stable and higher bandwidth utilization than typical algorithms in various networks with heterogeneous characteristics such as delay, bandwidth, and jitter.

In recent years, intelligent and automated device control features have led to a significant increase in the adoption of smart home IoT systems. Each IoT device sends its events to (and receives commands from) the corresponding IoT server/platform, which executes automation rules set by the user. Recent studies have shown that IoT messages, including events and commands, are subject to stealthy delays ranging from several seconds to minutes, or even hours, without raising any alerts. Exploiting this vulnerability, adversaries can intentionally delay crucial events (e.g., fire alarms) or commands (e.g., locking a door), as well as alter the order of IoT messages that dictate automation rule execution. This manipulation can deceive IoT servers, leading to incorrect command issuance and jeopardizing smart home safety. In this paper, we present MP-Mediator, which is the first defense system that can detect and handle the new, stealthy, and widely applicable delay attacks on IoT messages. For IoT devices lacking accessible APIs, we propose innovative methods leveraging virtual devices and virtual rules as a bridge for indirect integration with MP-Mediator. Furthermore, a VPN-based component is proposed to handle command delay attacks on critical links. We implement and evaluate MP-Mediator in a real-world smart home testbed with twenty-two popular IoT devices and two major IoT automation platforms (IFTTT and Samsung SmartThings). The experimental results show that MP-Mediator can quickly and accurately detect the delay attacks on both IoT events and commands with a precision of more than 96% and a recall of 100%, as well as effectively handle the delay attacks.

Summary With the development of mobile communication, Android software has increased sharply. Meanwhile, more and more malware emerges. Identifying malware in time is very important. Currently, most malware identifying methods are static, and the detection accuracy mainly depends on the classification feature and the algorithm. In order to improve the detection accuracy, reducing the dimension and difficulty of feature extraction, we propose a lightweight Android malware detection method based on sensitive features combination. After fully analyzing the static features in Android software, we improve the extraction methods of various features, define four sensitive features, and then form a sensitive features combination to more accurately reflect the characteristics of Android software with fewer features. Finally, four different machine learning classification algorithms were used to evaluate the classification effect of the sensitive features combination. The experiments show that the sensitive features combination has a good classification effect. When combined with the random forest classification algorithm, the accuracy is the highest, which could reach 97.6%.

Context-based paring is a promising direction for pairing IoT devices constrained in user interfaces (UIs). However, it takes a proximate distance or a long time for IoT devices to sense highly correlated context with enough entropy. In this work, we present a fast and secure approach, namedMPairing, to pairing multiple commercial off-the-shelf (COTS) IoT devices. This approach is based on the key idea that devices co-located within aphysically-secure boundarycan perceive qualified context under the help of human-in-the-loop (HITL). Specifically, we leverage received-signal-strength (RSS) trajectory data with manually-generated interference in a short period as the shared secret to achieve fast and secure pairing. Subsequently, the real-time RSS trajectory data is utilized to generate random numbers in lieu of pre-shared key (PSK), which makes our scheme more resistant to background attacks. We theoretically prove the security of our pairing scheme and implement it in real-world environments. Our experimental results demonstrate that our scheme can effectively defend against malicious devices by imposing a threshold on the similarity of RSS trajectory data. The experimental results also show that, compared with the traditional context-based pairing that takes up to 24 hours, in our scheme it takes only 10 seconds on average for a legitimate device to pass the similarity checking, which is efficient and robust.

Recently, visible light positioning and visible light communication are becoming a promising technology for integrated sensing and communication. However, the isolated design of positioning and communication has limited the system efficiency and performance. In this article, a visible light integrated positioning and communication (VIPAC) framework is formulated, in which the positioning task for the sensing service and the channel estimation task for the communication service are integrated into a unified architecture. First, a multi-task learning architecture, which is composed of a sparsity-aware shared network and two task-oriented sub-networks, is proposed to fully exploit the inherent sparse features of visible light channels, and achieve mutual benefits between the two tasks. The depth of the shared network can be adaptively adjusted to extract the optimal shared features, and the two sub-networks are further optimized for the two tasks, respectively. Moreover, the emerging federated learning technique is introduced to devise a multi-user cooperative VIPAC scheme, which further improves the generalization ability in spatiotemporally nonstationary environments while preserving data privacy. It is shown by theoretical analysis and simulation results that, the proposed scheme can significantly improve the performance of positioning and channel estimation in spatiotemporally nonstationary environments compared with existing benchmark schemes.

Security and privacy concerns keep growing with the successful development of Internet of Things (IoT) and the booming deployment of smart homes. IoT devices are utilized cooperatively to enable the interactions between home surroundings and users’ daily lives, containing forensically-valuable information about what happens in smart homes, which can help introduce digital forensics into smart homes to alleviate the growing concerns. However, current IoT devices, apps, and platforms usually do not provide built-in capabilities for digital forensics. To overcome this limitation, we propose a non-intrusive solution (i.e., bringing no modification to IoT devices, apps, and platforms) of digital forensic service to provide Forensics-as-a-Service (FaaS) for smart homes. First, it leverages side-channel analysis on sniffed network traffic to monitor commands, actions, and states of IoT devices. Then, it introduces provenance graphs (i.e., causal graphs) for smart home modeling to provide a holistic and overall explanation of smart homes. Machine learning (ML) techniques are applied to overcome the deficiency of a non-intrusive solution as it suffers from challenges in data collection and smart home modeling. Finally, it conducts forensic analysis based on scalable, reusable policies that are designed for graph-based smart home modeling. We implement a prototype of our forensic service and evaluate it in a real-world smart home. The evaluation results show that our forensic service can effectively collect forensic data for smart home modeling and conduct forensic analysis to explain security risks in smart homes.

This paper unveils a set of new attacks against Internet of Things (IoT) automation systems. We first propose two novel IoT attack primitives: Event Message Delay and Command Message Delay (event messages are generated by IoT devices to report device states, and command messages are used to control IoT devices). Our insight is that timeout detection in the TCP layer is decoupled from data protection in the Transport Layer Security (TLS) layer. As a result, even when a session is protected by TLS, its IoT event and/or command messages can still be significantly delayed without triggering alerts. It is worth highlighting that, by compromising/controlling one WiFi device in a smart environment, the attacker can delay the IoT messages of other non-compromised IoT devices; we thus call the attacks IoT Phantom-Delay Attacks. Our study shows the attack primitives can be used to build rich attacks and some of them can induce persistent effects. The presented attacks are very different from jamming. 1) Unlike jamming, our attacks do not discard any packets and thus do not trigger re-transmission. 2) Our attacks do not cause disconnection or timeout alerts. 3) Unlike reactive jamming, which usually relies on special hardware, our attacks can be launched from an ordinary WiFi device. Our evaluation involves 50 popular IoT devices and demonstrates that they are all vulnerable to the phantom-delay attacks. Finally, we discuss the countermeasures. We have contacted multiple IoT platforms regarding the vulnerable IoT timeout behaviors, and Google, Ring and SimpliSafe have acknowledged the problem.

With the widespread use of mobile devices, security and privacy concerns have been attracting increasing attention. However, it is challenging for Android users to apply the appropriate settings to protect their devices. Therefore, a cloud-based solution is proposed in this paper, which works based on crowdsourcing to generate recommended security and privacy settings for Android users. Following the suggestions of security experts, ordinary users can readily understand how to properly set up their devices for security enforcement. Our solution collects security-related data from experts, and a weight-based algorithm is introduced to determine data priority and credibility. Finally, the recommended security and privacy settings are created based on these data. The evaluation results demonstrate that our solution can effectively improve the robustness and reliability of Android devices.

Security for Internet of Things requires balancing power consumption and memory usage in devices. In this work, we propose symmetric DICE-based schemes for distributed IoT systems, which aims to have effective security and recovery procedures through symmetric keys while achieving energy efficiency. Our security schemes utilize an efficient security primitive for IoT Device Identifier Composition Engine (DICE). Our schemes enhance security and flexibility in distributed IoT systems, allowing for a secure dynamic bootstrapping and a node recovery mechanism in IoT system.

Cognitive Radio (CR) enabled vehicles in Vehicle Networks can use multiple Radio Access Networks (RANs) for data transmission. The simultaneous use of multiple RANs for transmission requires the design of a specific multi-path transmission protocol. Many scholars have studied the scheduling algorithm to improve the quality of multi-path transmission. However, most of the existing scheduling algorithms are difficult to deal with the challenges brought by the diversity and heterogeneity of the vehicle network. To deal with these challenges, this paper proposes an IP layer Deep Reinforcement Learning (DRL) multi-path transmission scheduling algorithm named Adaptive Dynamic Scheduling Algorithm (ADSA), which can dynamically generate the optimal scheduling policy through the interaction between agent and network environment. This paper first models the data packet scheduling strategy of multi-path transmission into an optimization problem of multi-path transmission efficiency. Then this paper transforms the optimization problem into a DRL problem and finds the optimal scheduling strategy through DRL model training. This paper evaluates the network performance of ADSA in different network scenarios compared with traditional scheduling algorithms. Simulation results show that ADSA increases the throughput by 8.9 Mbps compared with the three traditional scheduling algorithms and reduces the transmission delay by 4.3 ms.

The rapid growth of air traffic flow has brought the airspace capacity close to saturation and, at the same time, has resulted in great stress for air traffic controllers. The 4- D trajectory-based operation system is an important solution to problems in the current civil aviation field. The system mainly relies on accurate 4-D trajectory prediction technology to share trajectory information among air traffic control, airlines, and aircraft to achieve coordinated decision-making between flight and control. However, due to the complexity of trajectory data processing, the current 4-D trajectory prediction technology cannot meet actual needs. Therefore, a data generation and prediction network model (DGPNM) is proposed. It integrates the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) and long-short-term memory (LSTM) neu-ral networks. With its outstanding performance, the LSTM neural network is utilized in both the generation module and the prediction module. The proposed model generates plenty of sample data to enlarge the train set, so overfitting could be reduced in the process of LSTM training. Experimental results prove that compared with other classical methods, the altitude prediction accuracy in the proposed model far exceeds that in current research results, which improves the prediction accuracy of the 4- D trajectory.

With the proliferation of Internet of Things (IoT) devices and platforms, it becomes a trend that IoT devices associated with different IoT platforms coexist in a smart home, demonstrating the following characteristics. First, a smart home may use more than one platform to support its devices and automation. Second, IoT devices of a home may transmit messages over different paths. By selectively delaying IoT messages, our study finds that two issues, inconsistency and disorder, can be exacerbated by attackers significantly. We then explore how these issues can be exploited and present seven types of exploitation, collectively referred to as Delay-based Automation Interference (DAI) attacks. DAI attacks cause home automation to yield incorrect interaction results, placing the IoT devices and smart home in insecure, unsafe, or unexpected states. It is worth highlighting that DAI attacks do not depend on any IoT implementation vulnerabilities or leaked keys/tokens, and they do not trigger alarms at any layers of the IoT protocol stack. To demonstrate and evaluate the new attacks, we set up two real-world testbeds, where commercial IoT devices and apps are deployed. The week-long experiments from both testbeds show that an attacker has adequate opportunities to launch DAI attacks that cause security or safety issues.

Modern communication and computing technology is the basic support of the industrial intelligent systems (IIS). As a key component of IIS, the smart port is essential to be offered low-complexity and high-reliability communication service, especially for driverless engineering vehicles. However, it is combined and nonconvex to find the optimal association between vehicles and the road side units (RSUs). Besides, due to the mobility of vehicles and the severe path loss of mmWave links, beam switching and reassociation between vehicles and RSUs are required frequently, which brings a great challenge to the communication for the IIS. A low complexity closed-loop strategy based on distributed cooperation for mmWave communication in IIS is proposed in this study, in which user association and beam tracking with the assistance of beam pools is proposed. Many-to-many user association is established based on distributed multiagent reinforcement learning, where the vehicle can independently select the set of serving RSUs based on the local observation without information exchange with others, reducing the signaling overhead and computational complexity while improving system throughput. Furthermore, multipoint-cooperation soft switching of beams based on beam tracking improves the reliability of mmWave communication with the smaller training cost. Extensive analysis and simulation results demonstrate that the proposed solution significantly reduces the complexity of the mmWave communication while improving the throughput and stability in IIS.

Recent developments in the Internet of Vehicles (IoV) enable interconnected vehicles to support ubiquitous services. Various emerging service applications are promising to increase the Quality of Experience (QoE) of users. On-board computation tasks generated by these applications have heavily overloaded the resource-constrained vehicles, forcing it to offload on-board tasks to other edge intelligence (EI)-assisted servers. However, excessive task offloading can lead to severe competition for communication and computation resources among vehicles, thereby increasing the processing latency, energy consumption, and system cost. To address these problems, we investigate the transmission-awareness and computing-sense uplink resource management problem and formulate it as a time-varying Markov decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL) is proposed to develop an intelligence-oriented edge offloading strategy. Specifically, the vehicle can flexibly choose the network access mode and offloading strategy through two different radio interfaces to offload tasks to multiaccess edge computing (MEC) servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive balance between these two aspects. Simulation results show that the proposed algorithm can significantly reduce the transmission latency and computation delay.

Due to the inherent openness of wireless channels and the restriction of communication resources and energy supply, the privacy protection of the sensing data transmission in the security-critical Internet of Medical Things (IoMT) has become a great challenge. In order to guarantee the privacy of IoMT sensing and transmission in a wireless wiretap channel and reduce the power consumption, a privacy-aware sensing and transmission scheme with the name of sparse-learning-based encryption and recovery (SLER) is proposed. The sparse sensing signal is compressed and encrypted at the IoMT devices in the encryption stage and transmitted to the network coordinator or edge devices, where the sparse signal is accurately recovered via sparse learning in the decryption stage. The encryption stage is conducted based on compressed sensing. The decryption stage utilizes a model-based sparsity-aware deep neural network to accurately recover the sensing signal, whose sparse features are extracted to decrease the required size of measurement signals and increase the spectrum efficiency. The secrecy performance of the proposed SLER algorithm is theoretically analyzed. Experiments of electrocardiogram (ECG) signal transmission are performed as a typical IoMT application. The experimental results show that the proposed scheme can effectively guarantee the transmission secrecy against eavesdropping, while improving the spectrum efficiency and energy efficiency compared to other existing methods.

Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain users’ sensitive information. When guaranteeing the utility of data, we need to consider privacy of users’ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification (PPDC) scheme for IoT. We put forth a new controllably linkable group signature (CL-GS) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance ofPPDC.

Summary With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single‐depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single‐depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA.

Cloud storage is a cost-effective platform to accommodate massive data at low cost. However, advances of cloud services propel data generation, which pushes storage servers to its limit. Deduplication is a popular technique enjoyed by most current cloud servers, which detects and deletes redundant data to save storage and bandwidth. For security concerns, proof-of-ownership (PoW) can be used to guarantee ownership of data such that no malicious user could pass deduplication easily or utilize such mechanism for malicious purposes. Generally, PoW is implemented in static data archive where the data file is supposed to be read-only. However, to satisfy users’ needs for dynamical manipulation on data and support real-time data services, it is required to devise efficient PoW for dynamic archive. Inspired by malleable signature, which offers authentication even after its committed message changes, we propose the notion of bidirectional and malleable proof-of-ownership ($\sf {BM\mbox{-}PoW}$) for the above challenge. Our proposed$\sf {BM\mbox{-}PoW}$consists of bidirectional PoW (${\mbox{B-PoW}}$), malleable PoW (${\mbox{M-PoW}}$) and dispute arbitration protocol$\sf {DAP}$. While our${\mbox{B-PoW}}$is proposed for a static setting, the${\mbox{M-PoW}}$caters specifically for dynamic manipulation of data. In addition, our proposed arbitration protocol$\sf {DAP}$achieves accountable redaction which can arbitrate the originality of file ownership. We provide the security analysis of our proposal, and performance evaluation that suggests our proposed${\mbox{B-PoW}}$is secure and efficient for large file in static data archive. In addition, our proposed${\mbox{M-PoW}}$achieves acceptable performance under dynamic setting where data is supposed to be outsourced first and updated later in dynamic data archive.

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. To address these issues, in this article, we introduce a framework of the self-tallying voting system in decentralized IoT based on blockchain. We propose a concrete construction and prove that the proposed system satisfies all the security requirements, including fairness, dispute-freeness, and maximal ballot secrecy. We simulate the algorithms on a laptop, an Android phone, and a Raspberry Pi to test the time consumption and evaluate the gas cost of each algorithm in a private blockchain as well. The implementation results demonstrate the practicability of our system.

With the construction of smart cities, the number of Internet of Things (IoT) devices is growing rapidly, leading to an explosive growth of malware designed for IoT devices. These malware pose a serious threat to the security of IoT devices. The traditional malware classification methods mainly rely on feature engineering. To improve accuracy, a large number of different types of features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve these issues, a malware classification method based on Word2Vec and Multilayer Perception (MLP) is proposed in this article. First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file. Second, we combine these vectors into a 256x256x2-dimensional matrix. Finally, we designed a deep learning network structure based on MLP to train the model. Then the model is used to classify the testing samples. The experimental results prove that the method has a high accuracy of 99.54%.

Vehicle platoon, a novel vehicle driving paradigm that organizes a group of vehicles in the nose-to-tail structure, has been considered as a potential solution to reduce traffic congestion and increase travel comfort. In such a platoon system, head vehicles’ performances are usually evaluated by user vehicles’ feedbacks. Selection of an appropriate and reliable head vehicle while not disclosing user vehicles’ privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving platoon recommendation scheme, called TPPR, to enable potential user vehicles to avoid selecting the malicious head vehicles. The basic concept of TPPR is that each user vehicle holds a trust value, and the reputation score of the head vehicle is calculated via a truth discovery process. To preserve vehicles’ privacy, pseudonyms and Paillier cryptosystem are applied. In addition, novel authentication protocols are designed to ensure that only the valid vehicles (i.e., the vehicles holding the truthful trust values and joining the vehicle platoon) can pass the authentication. A comprehensive security analysis is conducted to prove that the proposed TPPR scheme is secure against several sophisticated attacks in vehicular ad hoc networks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.

In the era of the Internet of Things, the construction of edge computing platform has become more and more important, which has led lots of object detection applications being deployed on embedded devices. However, traditional object detection algorithms require lots of engery and a large amount of well labeled samples for training. The time spent on model training and data labeling also slows down the upgrade iteration of applications. Therefore, an object detection algorithm that requires only few energy and a few samples to update parameters could help the long-term benign development of IoT technology. In this paper, we propose an effective object detection method based on the few-shot learning, which could achieve considerable performance with few data for novel(new) classes. Our well-designed strategies could alleviate the impact of scale variation in support set under few-shot setting. Through extensive experiments, we prove that our model is superior to well-recognized baselines on few-shot object detection task.

In dynamic spectrum access (DSA), secondary users (SU) should only be allowed to access a licensed band belonging to incumbent users (IU) when the quality-of-service (QoS) requirements of both IUs and SUs can be satisfied at the same time. However, IU’s location and its received interference strength are considered sensitive in many DSA systems which should not be revealed, making it very challenging to optimize the network utility subjected to satisfying the operation and security requirements of SUs and IUs. In this paper, we develop a secure and distributed SU transmit power control algorithm to solve this challenge. Our algorithm achieves optimal SU power control to maximize the sum of SU rates. The SINR-guaranteed coexistence between SUs and IUs are enabled to maintain effective communication, while no information is directly required from IUs. Local measurements of IU signals provided by Environmental sensing capability (ESC) also undergo a security masking process to ensure that IU location cannot be derived from its outputs. Convergence and stability properties of our algorithm and its privacy-protection strength are both theoretically analyzed and experimentally evaluated through simulations.

With a built-in intelligent personal voice assistant providing Q&A services, smart speaker ecosystems combine multiple compatible components, including the internet of things (IoT) technology, mobile devices, and cloud computing. However, as it is closely related to people's daily lives, security and privacy issues have gained worldwide attention. Components in the ecosystem are interconnected and chained together to enable the ecosystem to perform increasingly diverse operations. By collecting meaningful data from smart speaker ecosystems, we can reconstruct user behavior and provide a holistic explanation for finding the root cause of an observable symptom. This highlights the need for digital forensic research to enhance the security and privacy of smart speaker ecosystems. In this paper, we first discuss the digital forensic characteristics of a smart speaker ecosystem. Then, we propose a proof-of-concept digital forensic tool based on data provenance, that supports the identification, acquisition, and analysis of client-side artifacts from local devices.

The smart grid is already widespread for the purpose of managing energy generation and distribution. In this system, frequent interactions between devices generate mass data, which requires robust processing capability. Fog computing has the advantage of latency and accessibility that can be applied in smart grids for improving data throughput and energy management efficiency to make the system sustainable. In addition, smart meters (SMs) are responsible for collecting real-time power consumption reports, and then sending them to the service provider (SP). The SP adjusts the power distribution strategy and develops an energy management optimization plan. Unfortunately, this process can reveal sensitive information that the user does not want to disclose. Therefore, we propose a conditional privacy-preserving data aggregation with batch verification scheme. Firstly, as an aggregator, fog devices aggregate masses of data from users into one with simple operation, which reduces communication complexity. Meanwhile, it supports batch verification on the SP side. Secondly, the scheme not only avoids certificates management and key escrow, but implements encrypting and signing in a logical step, thus satisfying confidentiality, integrity and authentication. Finally, our scheme provides conditional privacy-preserving, in which messages can be authenticated anonymously and malicious messages can be traced. Extensive performance evaluation details our scheme is efficient with low computation complexity and communication overheads.

With the widespread use of Android devices, research on their security has attracted increasing attention. However, at present, digital forensics for investigating attacks, such as social engineering attacks and phishing that target Android users, remains a challenging and time-consuming task. To help discover the existence of an attack and conduct effective investigations, we propose a top-down digital forensic tool for Android applications to reconstruct attack scenarios by considering both high-level user interface (UI) elements and low-level system events. Thus, we can explain the nature of an attack from a visual and global perspective. The tested evaluation results show that our tool can successfully reconstruct scenarios on Android devices for phishing attacks.

Federated Learning (FL), which allows multiple participants to co-train machine Learning models without exposing local data, has been recognized as a promising method in the past few years. However, in the FL process, the server side may steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preservation FL schemes seldom deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. Homomorphic encryption and Multi-Krum technology are combined to achieve ciphertext-level model aggregation and model filtering, which can guarantee the verifiability of local models while realizing privacy-preservation. Security analysis and performance evaluation prove that the proposed scheme can achieve enhanced security and improve the performance of the FL model.

Multipath transmission is considered one of the promising solutions to improve wireless resource utilization where there are many kinds of heterogeneous networks around. Most scheduling algorithms rely on real-time network metrics, including delay, packet loss, and arrival rates, and achieve satisfying results in simulation or wired environments. However, the implicit premise of a scheduling algorithm may conflict with the characteristics of real heterogeneous wireless networks, which has been ignored before. This paper analyzes the real network metrics of three Chinese heterogeneous wireless networks under different transmission rates. To make the results more convincing, we conduct experiments in various scenarios, including different locations, different times of the day, different numbers of users, and different motion speeds. Further, we verify the suitability of a typical delay-aware multipath scheduling algorithm, Lowest Round Trip Time, in heterogeneous networks based on the actual data measured above. Finally, we conclude the characteristics of heterogeneous wireless networks, which need to be considered in a well-designed multipath scheduling algorithm.

With the rapid development of the Internet of Things technology, smart speakers have become increasingly popular. However, smart speaker security is an ensuing threat. At present, smart speakers are activated by voice, and they monitor users’ voices 24 hours per day. Consequently, there may be problems with user privacy leakage. In this paper, we propose a non-intrusive digital forensic method for smart speakers. The main contribution of the paper is an effective method of combining network traffic analysis with the extraction of user intent and alarms about abnormal network traffic to support the investigation of security. We use Xiaomi smart speakers as an example in an experiment to verify our forensic method. The evaluation results show that our method works well for detecting security risks.

Each bank has different clients and each client may have transactions with multiple banks. Hence, clients’ data in a single bank may be partial and incomplete. If the data can be combined, each bank obtains comprehensive information, so as to better carry out business and enhance the quality of service, such as recommending financial products and inquiring about personal credit records. However, after the promulgation of GDPR by European Union in 2018, it is illegal to directly consolidate data crossing enterprises due to privacy and security concerns, especially for privacy-sensitive industries. Emerging federated learning(FL) is very suitable for secure data sharing for distributed banks in privacy. To prevent from connection of clients’ data and the certain bank, we adopt anonymity mechanism to hide the real identity of banks. In this paper, we first propose blockchain-empowered secure federated learning for distributed banks based on multi-party computation(MPC) with multi-key fully-homomorphic encryption(FHE) scheme. Then, we give detailed description of multi-key FHE based MPC protocol, anonymity mechanism and permissioned blockchain consensus protocol. Finally, we analyze the security and compare our scheme with several existed schemes. Numerical results show that the proposed data sharing scheme has good performance in terms of computational overhead and model accuracy.

In recent years, more and more medical institutions have been using electronic medical records (EMRs) to improve service efficiency and reduce storage cost. However, it is difficult for medical institutions with different management methods to share medical data. The medical data of patients is easy to be abused, and there are security risks of privacy data leakage. The above problems seriously impede the sharing of medical data. To solve these problems, we propose a blockchain-based medical data sharing scheme with attribute-based searchable encryption, named BMDS. In BMDS, encrypted EMRs are securely stored in the interplanetary file system (IPFS), while corresponding indexes and other information are stored in a medical consortium blockchain. The proposed BMDS has the features of tamper-proof, privacy preservation, verifiability and secure key management, and there is no single point of failure. The performance evaluation of computational overhead and security analysis show that the proposed BMDS has more comprehensive security features and practicability.

As the demand for real-time and high-quality social network services in mobile communications continues to grow, the performance defects of single-path transmission networks have become more and more prominent. At the same time, multipath transmission provides people with the possibility of stable and smooth communication in mobile wireless social networks. However, since it is usually difficult to obtain frequently varying delays along each path, packets always appear out of order during the communication of heterogeneous social networks, which will cause additional waiting delays in the receiving process. Therefore, it is still a very challenging task to construct a high-bandwidth and low-latency multipath transmission mechanism for social networks in mobile communications. According to the behavioral features of packets of social networks in mobile scenarios, a social network model BAH that reveals wireless social networks bottlenecks is established. Subsequently, this paper proposes a bottleneck-aware algorithm BFDE, which utilizes the one-way delay of periodically probing to derive the features of the wireless social networks bottleneck, so as to achieve an accurate estimation of the delay of each path. In the analysis and simulation, we compared it with the baseline EDPF and proved that the BFDE algorithm can achieve effective scheduling in complex and changeable mobile wireless social networks, thereby effectively increasing the multipath aggregation bandwidth, and has a strong robustness.

With the rapid growth of Internet of Things (IoTs), Internet-connected devices and home appliances gain popularity on the consumer electronic market. New home IoT products with built-in network connections and intelligent functionalities are quickly rolled out to the market. As predicted by Gartner, there will be more than 500 IoT devices deployed in a typical household by 2022. The easy device integration and advanced automation logic also brings new challenges with regard to security and privacy. IoT devices have been reported as unreliable because of the constraints in costs and resources. Anomalies of IoT devices include malfunctions of the physical part or the cyber part of an IoT device, as well as abnormal behaviors due to malicious attacks. Abnormal IoT devices could cause severe consequences, because they reside in the home environment and have critical functions that can change the physical world, such as door (smart lock) opening, smart oven burning (which could cause fire), or smart water valve opening (which could cause flooding). In this paper, we study the important issue of localizing the root cause of anomalies in a smart environment (e.g., smart homes and smart offices). We propose to use decision trees for efficient and effective anomaly root cause localization. We construct decision trees from automation rules that control the operations of smart IoT devices in a smart environment. Our performance evaluation on data collected from real smart homes demonstrate the effectiveness of our proposed approach.

With the booming deployment of smart homes, concerns about user privacy keep growing. Recent research has shown that encrypted wireless traffic of IoT devices can be exploited by packet-sniffing attacks to reveal users’ privacy-sensitive information (e.g., the time when residents leave their home and go to work), which may be used to launch further attacks (e.g., a break-in). To address the growing concerns, we propose SniffMislead, a non-intrusive (i.e., without modifying IoT devices, hubs, or platforms) privacy-protecting approach, based on packet injection, against wireless packet sniffers. Instead of randomly injecting packets, which is ineffective against a smarter attacker, SniffMislead proposes the notion of phantom users, “people” who do not exist in the physical world. From an attacker’s perspective, however, they are perceived as real users. SniffMislead places multiple phantom users in a smart home, which can effectively prevent an attacker from inferring useful information. We design a top-down approach to synthesize phantom users’ behaviors, construct the sequence of decoy device events and commands, and then inject corresponding packets into the home. We show how SniffMislead ensures logical integrity and contextual consistency of injected packets, as well as how it makes a phantom user indistinguishable from a real user. Our evaluation results from a smart home testbed demonstrate that SniffMislead significantly reduces an attacker’s privacy-inferring capabilities, bringing the accuracy from 94.8% down to 3.5%.

Advanced persistent threat (APT), with intense penetration, long duration, and high customization, has become one of the most grievous threats to cybersecurity. Furthermore, the design and development of Internet-of-Things (IoT) devices often do not focus on security, leading APT to extend to IoT, such as the Internet of emerging unmanned aerial vehicles (UAVs). Whether malware with attack payload can be successfully implanted into UAVs or not is the key to APT on the Internet of UAVs. APT malware on UAVs establishes communication with the command and control (C&C) server to achieve remote control for UAVs-aware information stealing. Existing effective methods detect malware by analyzing malicious behaviors generated during C&C communication. However, APT malware usually adopts a low-traffic attack mode, a large amount of normal traffic is mixed in each attack step, to avoid virus checking and killing. Therefore, it is difficult for traditional malware detection methods to discover APT malware on UAVs that carry weak abnormal signals. Fortunately, we found that most APT attacks use domain name system (DNS) to locate C&C server of malware for information transmission periodically. This behavior will leave some records in the network flow and DNS logs, which provides us with an opportunity to identify infected internal UAVs and external malicious domain names. This article proposes an APT malware on the Internet of UAVs detection method combining string matching and Fourier transformation based on DNS traffic, which is able to handle encrypted and obfuscated traffic due to packet payloads independence. We preprocessed the collected network traffic by converting DNS timestamps of DNS request to strings and used the trained random forest model to discover APT malware domain names based on features extracted through string-matching-based periodicity detection and Fourier transformation-based periodicity detection. The proposed method has been evaluated on the data set, including part of normal domains from the normal traffic and malicious domains marked by security experts from APT malware traffic. Experimental results have shown that our proposed detection method can achieve the accuracy of 94%, which is better than the periodicity detection algorithm alone. Moreover, the proposed method does not need to set the confidence to filter the periodicity with high confidence.

Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.

Federated learning (FL) serves as an enabling technology for intelligent edge computing, where high-quality machine learning (ML) models are collaboratively trained over large amounts of data generated by various Internet of Things devices while preserving data privacy. To further provide data confidentiality, computation auditability, and participant incentives, the blockchain framework has been incorporated into FL. However, it is an open question whether the model updates from participants in blockchain-assisted FL can disclose properties of the private data the participants are unintended to share. In this article, we propose a novel property inference attack that exploits the unintended property leakage in blockchain-assisted FL for intelligent edge computing. More specifically, we present an active attack to learn the property leakage from model updates of participants and to identify a set of participants with a certain property. We also design a dynamic participant selection strategy tailored to the setting of large-scale FL, which accelerates the selection process of target participants and improves attack accuracy. We evaluate the proposed attack through extensive experiments with publicly available data sets. The experimental results demonstrate that the proposed attack is effective and efficient in inferring various properties of training data, while maintaining the high quality of the main tasks in FL.

Internet of Underwater Things (IoUT) consists of numerous sensor nodes distributed in an underwater area for sensing, collecting, processing information, and sending related messages to the data processing center. However, the characteristics of the underwater environment will bring strict limitations on communication coverage and power scarcity to IoUT networks. Applying cooperative communications to IoUT networks can expand the communication range and alleviate power shortages. In this article, we investigate the cooperative communication problem in a power-limited cooperative IoUT system and propose a reinforcement learning-based underwater relay selection strategy. Specifically, we first determine the optimal transmit powers of the source node and the selected underwater relay to maximize the end-to-end signal-to-noise ratio of the system. Then, we formulate the underwater cooperative relaying process as a Markov process and apply reinforcement learning to obtain an effective underwater relay selection strategy. The simulation results show that the performance of the proposed scheme outperforms that of the equal transmit power settings under the same conditions. In addition, the proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality, and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this article proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle’s utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, which would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in IoV.

Nowadays, the advance of smart grid technology has fostered the development of microgrids, which can efficiently control and manage the distributed energy resources (DERs). In smart grid, IoT devices generate huge amounts of data, which are collected and shared among DERs, microgrids, and the main grid. To protect the shared data, it is necessary to implement the secure and efficient data access control. Ciphertext policy attribute-based encryption (CP-ABE) is a promising solution for the distributed system. However, lightweight IoT devices with limited computing capability cannot handle the computationally intensive ABE algorithms. To overcome this constraint, the decryption phase of CP-ABE is usually outsourced to the cloud, but this is inefficient and not safe enough in the distributed environment. In this article, we propose an edge blockchain empowered secure data access control scheme with fair accountability for the smart grid. The computation workloads of end user devices are outsourced to the edge nodes in a consortium blockchain system We adopt an on-chain/off-chain approach to ensure the flexible data sharing. Additionally, we adopt the threshold secret sharing scheme to establish a distributed authority. The security analysis and performance evaluation are conducted to prove the security and efficiency of our scheme. We use the Raspberry Pi to simulate lightweight IoT devices in the Hyperledger fabric platform to prove the usability of our scheme.

An online ride hailing (ORH) service creates a typical supply-and-demand two-sided market, which enables riders and drivers to establish optimized rides conveniently via mobile applications. Group ridesharing is a novel form of ridesharing, which allows a group of riders to share a vehicle that holds the minimum aggregate distance to the whole group. Accompanied by the advantage of ORH services, there comes some vital privacy concerns. In this article, we propose a privacy-preserving online group ridesharing matching scheme for ORH services, called PGRide. PGRide can select the nearest driver to serve a group of riders, without leaking the location privacy of both riders and drivers. In PGRide, we propose an encrypted aggregate distance computation approach by using somewhat homomorphic encryption with ciphertexts packing, which efficiently computes the aggregate distances from a group of riders to large-scale dynamic drivers in encrypted form. Meanwhile, we design a secure minimum selection protocol by using ciphertexts packing and blinding, which efficiently finds the minimum element from a set of encrypted integers without leaking any actual element value. Theoretical analysis and performance evaluations prove that PGRide is secure, accurate, and efficient.

Mobile-edge computing (MEC) has emerged as a new computing paradigm with great potential to alleviate resource limitations attributed to mobile device users (MDUs) by offloading intensive computations to ubiquitous MEC server. However, most of the current offloading policies allow MDUs to transmit their tasks to the same connected small base stations (sBSs), which invariably increases latency and limits performance gain due to overload. Moreover, the security issue mitigating sensitive communication of information is not adequately addressed. Therefore, in this study, in addition to proposing a joint load balancing and computation offloading (CO) technique for MEC systems, we introduce a new security layer to circumvent potential security issues. First, a load balancing algorithm for efficient redistribution of MDUs among sBSs is proposed. In addition, a new advanced encryption standard (AES) cryptographic technique suffused with electrocardiogram (ECG) signal-based encryption and decryption key is presented as a security layer to safeguard the vulnerability of data during the transmission. Furthermore, an integrated model of load balancing, CO and security is formulated as a problem whose goal is to decrease the time and energy demands of the system. Detailed experimental results prove that our model with and without the additional security layers can save about 68.2% and 72.4% of system consumption compared to the local execution.

Smart city, as a new mode, is introduced to improve the level of city management for modern cities. In smart cities, a kernel field is health management for urban residents. Hospital management, as one of the most important components in health management, is concerned. To provide high-quality medical service for sick residents, accurate patient health data analysis is needed. Thus, data collection in patient health monitoring is necessary. To achieve this, massive Internet-of-Things devices are distributed; in general, they are resource-constrained devices. From this, lightweight index generation is needed. Furthermore, with the development of professional technologies in medical science, the hospital manager has to employ many different types of professional doctors. They need the shared patient health data to do a precise diagnosis and present an efficient therapeutic schedule for each patient. However, many secret details are recorded in the patient health data. Thus, data privacy of the shared patient health data should be maintained. In this article, we propose a new traceable patient health data search system for hospital management in smart cities. In this system, the system manager shares the encrypted patient health data to different doctors at the grain of hospital bed. Each doctor accurately finds a patient with a special feature from the patient health monitoring data. To prevent patient health data leakage, the functions of illegal search query blocking and inside malicious user tracing are designed. The performance analysis shows that our system is practical for lightweight data collecting devices.

In developing smart city, the growing popularity of machine learning (ML) that appreciates high-quality training data sets generated from diverse Internet-of-Things (IoT) devices raises natural questions about the privacy guarantees that can be provided in such settings. Privacy-preserving ML training in an aggregation scenario enables a model demander to securely train ML models with the sensitive IoT data gathered from IoT devices. The existing solutions are generally server aided, cannot deal with the collusion threat between the servers or between the servers and data owners, and do not match the delicate environments of IoT. We propose a privacy-preserving ML training framework named Heda that consists of a library of building blocks based on partial homomorphic encryption, which enables constructing multiple privacy-preserving ML training protocols for the aggregation scenario without the assistance of untrusted servers, and defending the security under collusion situations. Rigorous security analysis demonstrates the proposed protocols can protect the privacy of each participant in the honest-but-curious model and guarantee the security under most collusion situations. Extensive experiments validate the efficiency of Heda, which achieves privacy-preserving ML training without losing the model accuracy.

IoT based eHealth system brings a revolution to healthcare industry, with which the old healthcare systems can be updated into smarter and more personalized ones. The practitioners can continue monitoring the physical status of the patients at anytime and anywhere, and develop more precise treatment plans by analyzing the collected data, such as heart rate, blood pressure, blood glucose. Actually, these smart sensors used in eHealth system are smart embedded devices (SED). Due to the limitations on hardware capabilities, these inter-connected SEDs lack of security considerations in design and implementation, and face the threats from the network. To prevent the malicious users (or programs) from tampering with the SEDs, trusted platform module (TPM) is adopted, which can guarantee the system integrity via detecting unauthorized modifications to data and system environment. However, due to the limited scalability and insufficient system resources, not all SEDs can be deployed with TPM chips. To address this issue, in this paper, a TPM extension scheme (xTSeH) is proposed. In xTSeH, we have extended the functions of a TPM deployed in a SED (TSED) to those non-TPM-protected SEDs (N-TSED) via network. A shadow TPM in the form of a kernel module is designed as the trust base for the N-TSED, which is the representative of the TPM in TSED. Then, three protocols are proposed to implement the integrity verification and inter-SED authentication. Finally, a Raspberry Pi based prototype system is designed and implemented. The feasibility and usability of our scheme are proved by the analysis of the experimental results of system performance.

Application repackaging is a severe threat to Android users and the market. Not only does it infringe on intellectual property, but it is also one of the most common ways of propagating mobile malware. Existing countermeasures mostly detect repackaging based on app similarity measurement, which tends to be imprecise when obfuscations are applied to repackaged apps. Moreover, they rely on a central party, typically the hosting app store, to perform the detection, but many app stores fail to commit proper effort to piracy detection. We consider building the application repackaging detection capability into apps, such that user devices are made use to detect repackaging in a decentralized fashion.The main challenge is how to protect the detection code from being manipulated by attacks. We propose a creative use oflogic bombs, which are otherwise regularly used in malware. Thetrigger conditionsof bombs are constructed to exploit the differences between the attacker and users, such that a bomb that lies dormant on the attacker side will be activated on the user side. The detection code, which is part of the bombpayload, is executed only if the bomb is activated. We introducecryptographically obfuscated logic bombto enhance the bomb: (1) the detection code iswoveninto the neighboring original app code, (2) the mixed code gets encrypted using a key, and (3) the key is deleted from the app and can only be derived when the bomb is activated. Thus, attacks that try to modify or delete the detection code will corrupt the app itself, and searching the key in the application will be in vain. Moreover, we propose abomb sprayingtechnique that allows many bombs to be injected into an app, multiplying the needed adversary effort for bypassing the detection. In addition to repackaging detection, we present application tampering detection to fight attacks that insert malicious code into repackaged apps. We have implemented a prototype, namedBombDroid, that builds repackaging and tampering detection into apps through bytecode instrumentation. The evaluation and the security analysis show that the technique is effective, efficient, and resilient to various bomb analysis techniques including fuzzing, symbolic execution, multi-path exploration, and program slicing. Ethical issues due to the use of logic bombs are also discussed.

Encrypted web traffic can reveal sensitive information of users, such as their browsing behaviors. Existing studies on encrypted traffic analysis focus on website fingerprinting. We claim that fine-grained webpage fingerprinting, which speculates specific webpages on a same website visited by a victim, allows exploiting more user private information, e.g., shopping interests in an online shopping mall. Since webpages from the same website usually have very similar traffic traces that make them indistinguishable, existing solutions may end up with low accuracy. In this paper, we propose FineWP, a novel fine-grained webpage fingerprinting method. We make an observation that the length information of packets in bidirectional client-server interactions can be distinctive features for webpage fingerprinting. The extracted features are then fed into traditional machine learning models to train classifiers, which achieve both high accuracy and low training overhead. We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP. The experimental results demonstrate that FineWP is superior to the state-of-the-art methods in terms of accuracy, time complexity and stability.

Decentralized Applications (DApps) are increasingly developed and deployed on blockchain platforms such as Ethereum. DApp fingerprinting can identify users' visits to specific DApps by analyzing the resulting network traffic, revealing much sensitive information about the users, such as their real identities, financial conditions and religious or political preferences. DApps deployed on the same platform usually adopt the same communication interface and similar traffic encryption settings, making the resulting traffic less discriminative. Existing encrypted traffic classification methods either require hand-crafted and fine-tuning features or suffer from low accuracy. It remains a challenging task to conduct DApp fingerprinting in an accurate and efficient way. In this paper, we present GraphDApp, a novel DApp fingerprinting method using Graph Neural Networks (GNNs). We propose a graph structure named Traffic Interaction Graph (TIG) as an information-rich representation of encrypted DApp flows, which implicitly reserves multiple dimensional features in bidirectional client-server interactions. Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier. We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows. The experimental results show that GraphDApp is superior to the other state-of-the-art methods in terms of classification accuracy in both closed- and open-world scenarios. In addition, GraphDApp maintains its high accuracy when being applied to the traditional mobile application classification.

In softwarized industrial networking, honeypot identification is very important for both the attacker and the defender. Existing honeypot identification relies on simple features of honeypot. There exist two challenges: The simple feature is easily simulated, which causes inaccurate results, whereas the advanced feature relies on high interactions, which lead to security risks. To cope with these challenges, in this article, we propose a secure fuzzy testing approach for honeypot identification inspired by vulnerability mining. It utilizes error handling to distinguish honeypots and real devices. Specifically, we adopt a novel identification architecture with two steps. First, a multiobject fuzzy testing is proposed. It adopts mutation rules and security rules to generate effective and secure probe packets. Then, these probe packets are used for scanning and identification. Experiments show that the fuzzy testing is effective and corresponding probe packet can acquire more features than other packets. These features are helpful for honeypot identification.

Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of convenient services in intelligent transportation systems, and yet vulnerable to various intrusion attacks. Intrusion detection systems (IDSs) can mitigate the security threats by detecting abnormal network behaviours. However, existing IDS solutions are limited to detect abnormal network behaviors under local sub-networks rather than the entire VANET. To address this problem, we utilize deep learning with generative adversarial networks and explore distributed SDN to design a collaborative intrusion detection system (CIDS) for VANETs, which enables multiple SDN controllers jointly train a global intrusion detection model for the entire network without directly exchanging their sub-network flows. We prove the correctness of our CIDS in both IID (Independent Identically Distribution) and non-IID situations, and also evaluate its performance through both theoretical analysis and experimental evaluation on a real-world dataset. Detailed experimental results validate that our CIDS is efficient and effective in intrusion detection for VANETs.

With the increasing demand to deploy convolutional neural networks (CNNs) on 5G mobile platforms, architecture designs with efficient sparse kernels (SKs) were proposed, which can save more parameters than the standard convolution while maintaining the high accuracy. Despite the great potential, neural network designs with SKs still require a lot of expert knowledge and take ample time. In this paper, we first propose a search scheme that effectively reduces the SK design space based on three aspects: composition, performance, and efficiency. Meanwhile, we completely eliminate the model training from our search scheme. Instead, an easily measurable quantity, the information field, is identified and used to predict the model accuracy in the searching process. Additionally, we provide a detailed efficiency analysis on the final designs found by our scheme. Second, based on the analysis we propose a model transformation scheme to better utilize the SK designs on existing models to either reduce the number of parameters or increase the accuracy. Last, considering the extra programming overhead and the expert knowledge required by the model transformation scheme, we develop a compiler prototype to automate the entire process, given the source code of an existing model. Experimental results show that models composed of the sparse kernel designs searched by our search scheme can beat state-of-the-art networks such as ResNets in terms of the accuracy and the efficiency. Also by using our model transformation scheme we can easily improve the accuracy (the same number of parameters) or the efficiency (the same accuracy) upon existing state-of-the-art models.

Stemming from the recent evolutionary progress of the 5G wireless communication and Internet of Things (IoT) relevant technologies, the vision of the Internet of Connected Vehicles (IoCV) has become more apparent. On the basis of the state-of-the-art IoCV conceptual implementations, the location of the vehicles is one of the essential driven data of IoCV, and the location privacy issue needs to be taken into account. However, when scrutinizing into IoCV, noticeable challenges of location-aware scenario has raised. For IoCV, location is more than just query criteria like in Location-based Services (LBSs) of mobile Internet. It is also the underpinning data of various types of IoCV underlying mechanisms and functions. This difference makes preserving location privacy in IoCV quite different from the traditional privacy scenarios. In this paper, an overall analysis of end-user location privacy in IoCV was performed. To solve the location privacy dilemma, we proposed an intent prediction-based approach named LocJury, which benefits from the emerging concept of Intent-based Networking (IBN). LocJury provides location privacy by learning and estimate the intent of location access and will penalize those malicious location accesses. By simulating the conceptual IBN-based IoCV application scenario, which relies on the location accesses, the performance of LocJury is evaluated under various circumstances. The simulation result verified the effectiveness of our proposed method.

The Internet of Vehicles (IoV) is one important application scenarios for the development of the Internet of things. The software-defined network (SDN) and fog computing could effectively improve the IoV network dynamics, which enables the application to achieve better performance by offloading some tasks to fog node or cloud center. Current computation offloading approaches for IoV and fog computing mostly focus on resource utilization. However, the energy-aware offloading has not been adequately addressed, especially for IoV systems with many battery-powered roadside units (RSU) and electric vehicles (EV). In this paper, we study the offloading problem in SDN and fog computing-based IoV systems. An energy-aware dynamic offloading scheme is proposed to prolong the running time of the IoV system by leveraging available battery power to execute more applications. The remaining battery power is defined as a dynamic weight factor in the execution cost model to adjust the optimization objective. Meanwhile, the dependence between applications is also taken into consideration in the cost model. A heuristic optimization algorithm is designed to solve the optimization problem. We conducted comprehensive experiments and results have shown that the offloading scheme could execute more applications with the available battery power under the constraints of application dependence.

To meet the increasing traffic demands characterized by large bandwidth and high burstiness, more traffic has been moving to inter-datacenter elastic optical networks (inter-DC EONs) for processing. The integration of two emerging paradigms, network function virtualization (NFV) and software-defined networking (SDN), enables Internet service providers (ISPs) to deploy service function chains (SFCs) from users flexibly while reducing operational and capital expenditures. This article focuses on the problem of online SFC provisioning in inter-DC-EONs with the aim of maximizing ISP profits, where the challenge in jointly allocating IT and spectrum resources when deploying SFCs is balanced with the deployment costs of processing as many user requests as possible. We design two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links. Simulation results show that, compared with the existing algorithm, our proposed algorithms significantly shorten the deployment time, improve total profit of ISP by up to 40% and reduce the blocking probability by up to 35%.

In recent years, with the rapid development of machine learning in various domains, more and more studies have shown that machine learning models are vulnerable to adversarial attacks. However, most existing researches on adversarial machine learning study non-graph data, such as images and text. Though some previous works on graph data have shown that adversaries can make graph-based classification methods unreliable by adding perturbations to features or adjacency matrices of existing nodes, these kinds of attacks sometimes have limitations for real-world applications. For example, to launch such attacks in real social networks, the attacker cannot force two good users to change (e.g., remove) the connection between them, which means that the attacker can not launch such attacks. In this paper, we propose a novel attack on collective classification methods by adding fake nodes into existing graphs. Our attack is more realistic and practical than the attack mentioned above. For instance, in a real social network, an attacker only needs to create some fake accounts and connect them to existing users without modifying the connections among existing users. We formulate the new attack as an optimization problem and utilize a gradient-based method to generate edges of newly added fake nodes. Our extensive experiments show that the attack can not only make new fake nodes evade detection, but also make the detector misclassify most of the target nodes. The proposed new attack is very effective and can achieve up to 100% False Negative Rates (FNRs) for both the new node set and the target node set.

Internet of Thing platforms prosper home automation applications (apps). Prior research concerns intra-app security. Our work reveals that automation apps, even secured individually, still cause a family of threats when they interplay, termed as Cross-App Interference (CAI) threats. We systematically categorize such threats and encode them using satisfiability modulo theories (SMT). We present HomeGuard, a system for detecting and handling CAI threats in real deployments. A symbolic executor is built to extract rule semantics, and instrumentation is utilized to capture configuration during app installation. Rules and configuration are checked against SMT models, the solutions of which indicate the existence of corresponding CAI threats. We further combine app functionalities, device attributes and CAI types to label the risk level of CAI instances. In our evaluation, HomeGuard discovers 663 CAI instances from 146 SmartThings market apps, imposing minor latency upon app installation and no runtime overhead.

The popularity of electric vehicles depends on convenient and efficient charging services. At present, none of existing charging services allow users to reach charging stations at desirable time and charge immediately when they arrive without waiting. This paper proposes a charging reservation service approach based on the consortium blockchain and smart contract technology. Users can choose the charging station and charging time period with no charging congestion, which is based on the charging information recorded in the consortium blockchain composed of stations located in distributed regions in a city. To ensure a user arrives at the charging station on time and charge within due time as he/she has reserved, a personalized pricing scheme for reward and punishment by utilizing smart contract is proposed. We take the past charging behavior into consideration when deciding current charging price of each user, which can provide individualized prices for different users. This approach can not only greatly reduce the user's waiting time, but also offer high cost-effective charging services for good behavior users. We carry out experimental verification under multiple sets of parameter settings, illustrate the variations in three aspects including user's initial score, violation rate and intensity of reward and punishment, thus the feasibility of our approach is proved. Our work is a credible charging paradigm based on trust mechanism via blockchain, which has the potential to become an industry service standard for electric vehicle charging.

With the development of cloud computing and wireless networks, cloud storage services are widely used in daily life. People can get access to cloud storage anytime and anywhere. Cloud storage forensics is a computer forensic scenario that currently appears frequently. In this paper, we first briefly introduce the general methods of traditional cloud storage forensics and then introduce forensic investigations that are conducted on three cloud storage services: BaiduNetDisk, 115yun, and WeiYun. The findings of the forensic analysis are presented in detail to help the development of forensics for cloud storage services.

In recent years, multimedia has been widely used in the medical and health fields and plays a pivotal role in personal health records (PHR). In the PHR system, people can easily access to their PHR data, which is mainly composed of X-ray, ECG and other multimedia data, by mobile devices. Owing to the need for efficiently accessing to the PHR data, the hybrid cloud-fog system is very suitable for the storage of PHR data because the computing capacity in this system is extended to the edge of the network to achieve low delay. PHR data generally contains a lot of sensitive information that may be directly used for medical diagnosis, so it demands more security capacity to ensure the fine-grained access control, confidentiality and validity of data, while protecting the privacy of data owners. Moreover, PHR systems should allow data owners to share their PHR data with specific institutions or individuals. For resolving the above problems, we propose a secure fog-assisted sharing scheme for multimedia PHR in the cloud based on multi-authority attribute-based signcryption, called SFSP. In the scheme, outsourcing computing is involved to transfer some computation to fog nodes to reduce the computation overhead on the user side in the signcryption and decryption stages because of the limited computing power of normal users' mobile devices. Performance analysis shows that SFSP is more suitable for the sharing of PHR in the cloud due to its higher efficiency than the existing schemes.

Electronic medical records (EMRs) can provide medical institutes with a number of research materials, which could promote the development of medical field. In order to protect patients' privacy while achieving efficient medical data sharing, we propose a privacy-preserving medical data sharing scheme based on consortium blockchain. In the scheme, a conditional anonymous tracing mechanism is designed to protect users' privacy, in which malicious users can be tracked when illegal behavior occurs. However, as the number of users grows, masses of medical data might touch the storage bottleneck of blockchain. Therefore, an on-chain-off-chain storage model is designed to alleviate the storage burden of blockchain, in which only the metadata of EMRs are recorded in blockchain while the patients' EMRs are stored in cloud after being encrypted. In order to resist the collusion attack, an improved proxy re-encryption scheme is introduced to ensure that EMRs can only be accessed by the authorized data requester. Moreover, security analysis and simulation demonstrate that the proposed scheme is more secure and efficient.

As the demand of mobile services for high bandwidth grows, aggregation of various wireless network resources in hybrid communication system becomes a trend. Network coding has been widely studied to solve the packet reordering, low aggregation efficiency problems which are brought by network heterogeneity. Even if the determination of coding scale is the core issue of network coding, current research can not adapt to heterogeneous wireless channels, resulting in low bandwidth aggregation and decoding efficiency. Therefore, we propose a cross-layer network coding scheme called Delay Determined Group Size (DDGS) scheme, which adaptively adjusts the coding scale to solve the problem of overall performance degradation caused by heterogeneous characteristics of wireless channels. It can get the utmost out of the performance improvement brought by network coding, to better avoid packet reordering as well as reduce the receiving delay at the receiving end. The simulation results show that DDGS is significantly superior to network coding schemes in existing state-of-the-art solutions.

With the development of information and communication technologies in smart grid, peer-to-peer (P2P) energy trading for distributed energy resources (DER) has achieved an efficient two-way flow of information and power. The adoption of blockchain technology makes the P2P energy trading more secure and transparent. Considering that users with extra energy may be reluctant to participate in the energy trading due to privacy concerns, many researchers have focused on potential privacy issues. However, most of the existing works are built on top of a semi-decentralized energy blockchain in which only a few certified third-party nodes are authorized to manage and verify transactions - once these authorized nodes are attacked, the system will be threatened. In this paper, we use the Ciphertext Policy Attribute-Based Encryption (CP-ABE) scheme to establish a blockchain based P2P energy trading approach with privacy preservation, which allows peer nodes, including sellers and purchasers, to manage and verify transactions autonomously without needing any additional third-party nodes. In addition, we introduce the redactable blockchain technology into our scheme to ensure users can modify their sensitive information uploaded to the blockchain. Furthermore, we improve the CP-ABE scheme to provide low latency in the system. The experimental evaluations show that our scheme is efficient and practical.

In social networks, some private information, such as the personal name, age gender, the number of friends, can be obtained by others. This paper defines a combination degree-neighborhood label matching attack model based on group maps obtained from multi-social networks. Based on the heuristic combination degree attack model, the target combination degree and neighborhood labels are used as the background knowledge of the attacker to obtain the candidate vertices set. The singularity of the sensitive label matching results will expose the sensitive information of the vertex being attacked. In order to solve this privacy attack, this paper proposes a group graph sensitive label generalization L diversity algorithm. This algorithm reduces the probability of sensitive labels being identified by designing a group map sensitive label generalization tree. According to the background knowledge, the number of sensitive labels in the candidate set and the number of sensitive labels obtained by matching are not less than L, so as to protect the sensitive information of the attacked target. The algorithm was evaluated by using three sets of data with different ratios. The experiment results show that the privacy protection algorithm effectively prevents sensitive label privacy attacks consisting of combination degree-domain label matching and better maintains the availability of graph data.

The Electronic Health Record (EHR) has been widely used in cloud-based medical data platforms. Since the owner of the EHR is a patient and the manager is a doctor (or hospital), this separation of ownership of privacy data leads to privacy leakages of the EHR stored in the cloud environment. The tamper-proof and traceable features of the blockchain make it a promising way to solve EHR privacy protection issues. Nevertheless, the latest research findings on blockchain-based schemes for EHR privacy preservation are focused on single blockchain that corresponds to a designated medical institution, which are not compatible with the privacy anti-leakage demands since private data is transferred through multiple blockchains. In this regard, we put forward relay-chain as a service (RaaS), and propose a cross-blockchain based EHR Privacy-preserving scheme (CEPS), which uses relay-chain to achieve secure access to EHR data when patients visit different hospitals. Furthermore, our scheme ensures that patients can delete the link of EHR freely and effectively. Security analysis and performance evaluations are performed, which prove that CEPS is highly secure and efficient impressively.

Due to malicious eavesdropping, forgery as well as other risks, it is challenging to dispose and store collected power data from smart grid in secure manners. Blockchain technology has become a novel method to solve the above problems because of its de-centralization and tamper-proof characteristics. It is especially well known that data stored in blockchain cannot be changed, so it is vital to seek out perfect mechanisms to ensure that data are compliant with high quality (namely, accuracy of the power data) before being stored in blockchain. This will help avoid losses due to low-quality data modification or deletion as needed in smart grid. Thus, we apply the parallel vision theory on the identification of meter readings to realize accurate power data. A cloud-blockchain fusion model (CBFM) is proposed for the storage of accurate power data, allowing for secure conducting of flexible transactions. Only power data calculated by parallel visual system instead of image data collected originally via robot would be stored in blockchain. Hence, we define the quality assurance before data uploaded to blockchain and security guarantee after data stored in blockchain as a successive framework, which is a brand new solution to manage efficiency and security as a whole for power data and data alike in other scenes. Security analysis and performance evaluations are performed, which prove that CBFM is highly secure and efficient impressively.

The Internet of Things (IoT) features pervasive sensing and control capabilities by deploying a massive machine-type communication (MTC) devices. However, low hardware complexity, low processing capacity and constrained energy of MTC devices raise lots of challenges to communication and security. Therefore, physical layer security (PLS) appears to be a promising method for securing wireless communication in which orthogonal frequency division multiplexing (OFDM) is widely used. However, most existing OFDM-based PLS schemes possess little dynamicity for their secret keys derived from pre-extracted information. The combination of OFDM and compressed sensing (CS) is a remarkable approach to address the weakness. So, with compressed sensing, we propose a physical layer security scheme for OFDM-based IoT systems, named PLSSCS. It can address the demerit of key extraction by channel measuring instead of pre-extracted information. To evaluate the performance of PLSSCS, the simulation is carried out in MATLAB, and PLSSCS provides better performance with dynamic property.

As a typical application scenarios of Internet of Things (IoT), Internet of Vehicles (IoV) is playing an important role in the field of intelligent transportation. In such a system, vehicles are allowed to collect ambient sensing data and communicate with other vehicles, roadside infrastructure, cloud server, even personal mobile smart devices in order to exchange the relevant traffic information that usually includes the owners' sensitive information, such as geographical location or personally identifiable information. However, in open network environment, different wireless communication technologies make all these information vulnerable to various attacks and compromises. The users' private information is put at the risk of leaking at any time. To address this issue, a blockchain-based privacy-preserving traffic data sharing protocol with cloud storage is proposed based on a novel heterogeneous signcryption scheme between Public Key Infrastructure (PKI) and Certificateless Cryptosystem (CLC). Through theoretical and simulation analysis in terms of security, complexity and time consumption, the proposed traffic data sharing protocol is proved to meet the security requirements of data tamper-proofing, anonymity and traceability with higher efficiency.

With the increasing demand of vehicular data transmission, the utilization of cellular resources in low frequency bands is facing great challenges to meet the growing throughput requirements of cellular vehicle-to-everything (C-V2X) users. To solve this problem, we expand certain aspects of the vehicular business to the unlicensed spectrum, which enables C-V2X users to access unlicensed channels fairly and thus will greatly increase system capacity. Moreover, this approach also introduces coexistence issues between C-V2X users and unlicensed users. In this paper, a C-V2X and Wi-Fi coexistence scheme based on reinforcement learning is proposed while considering the system throughput and fairness. A Q-learning algorithm is utilized to determine the optimal duty cycle selection strategy in a multi-unlicensed-channels scenario. Simulation results show that compared with existing coexistence schemes, the proposed scheme can improve throughput performance considerably while ensuring fairness.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

With the dramatic increase of video traffic on the Internet, video quality of experience (QoE) measurement becomes even more important, which provides network operators with an insight into the quality of their video delivery services. The widespread adoption of end-to-end encryption protocols such as SSL/TLS, however, sets a barrier to QoE monitoring as the most valuable indicators in cleartext traffic are no longer available after encryption. Existing studies on video QoE measurement in encrypted traffic support only coarse-grained QoE metrics or suffer from low accuracy. In this paper, we propose DeepQoE, a new approach that enables real-time video QoE measurement from encrypted traffic. We summarize critical fine-grained QoE metrics, including startup delay, rebuffering, and video resolutions. In order to achieve accurate and real-time inference of these metrics, we build DeepQoE by employing Convolutional Neural Networks (CNNs) with a sophisticated input and architecture design. More specifically, DeepQoE only leverages packet Round-Trip Time (RTT) in upstream traffic as its input. Evaluation results with real-world datasets collected from two popular content providers (i.e., YouTube and Bilibili) show that DeepQoE can improve QoE measurement accuracy by up to 22% over the state-of-the-art methods.

With the development of communication technologies, vehicular network applications have evolved from basic traffic safety and efficiency applications to information and entertainment applications. The implementation of emerging vehicular applications is based on the efficient dissemination of multimedia data. In view of the dynamic topology changes, severe channel fading and limited spectrum resources of vehicular networks, how to achieve efficient multimedia data dissemination in the harsh network environment is an urgent problem. Based on the hybrid cellular-D2D vehicular network, this paper proposes a cluster-based cooperative multicast scheme. The scheme combines multicast transmission with D2D-assisted relay technology to provide high-quality data dissemination for vehicle users under limited spectrum resources. In this paper, we innovatively present a communication quality index that considers multiple performance factors and formulate the relay selection problem as the anti p-center problem in graph theory. Then we propose a heuristic method to solve the problem. The results show that the proposed scheme can effectively improve the utilization of wireless resources and the success rate of data dissemination.

The advancement of the wireless body area networks (WBAN) and sensor technologies allows us to collect a variety of physiological and behavioral data from human body. And appropriate application of machine learning methods can greatly promote the development of e-health. Nevertheless, the collected data contains personal privacy information. When using the machine learning methods to analyze the collected data, some information of the training data will be stored in the learning models unconsciously. To handle such information disclosure problem, we propose a differentially private classification algorithm based on ensemble decision tree with high utility for wireless body area networks. In order to improve the accuracy and stableness of classification, the bagging framework of ensemble learning is used in our algorithm. We aggregate the results of multiple private decision trees as the final classification in a weight-based voting way. For each private decision tree trained on the bootstrap samples, we offer a novel privacy budget allocation strategy that allows the nodes in larger depth to get more privacy budget, which can mitigate the problem of excessive noise introduced to leaf nodes to some extent. The better classification accuracy and stableness of this new algorithm, especially on small dataset, are demonstrated by simulation experiments.

Machine Learning (ML) plays very significant role in the Internet of Things (IoT) cybersecurity for malicious and intrusion traffic identification. In other words, ML algorithms are widely applied for IoT traffic identification in IoT risk management . However, due to inaccurate feature selection, ML techniques misclassify a number of malicious traffic in smart IoT network for secured smart applications. To address the problem, it is very important to select features set that carry enough information for accurate smart IoT anomaly and intrusion traffic identification. In this paper, we firstly applied bijective soft set for effective feature selection to select effective features, and then we proposed a novel CorrACC feature selection metric approach. Afterward, we designed and developed a new feature selection algorithm named Corracc based on CorrACC, which is based on wrapper technique to filter the features and select effective feature for a particular ML classifier by using ACC metric. For the evaluation our proposed approaches, we used four different ML classifiers on the BoT-IoT dataset. Experimental results obtained by our algorithms are promising and can achieve more than 95% accuracy.

The challenges of population management as urban density increase globally have compelled researchers and developers to consider more efficient means of managing resources in cities. Consequently, the smart city concept has emerged as a response to addressing the challenge of optimal resource utilization in urban centers. However, with digital technologies proliferating as key components of the solution, it is necessary to develop a digital identity solution for all components of the smart city environment. For completeness, the solution must encompass all entities, including physical and intangible assets, processes, and most importantly, its residents. Consequently, a unified, distributed data integration and efficient analysis platform is required: the digital city operating system. In this article, we focus on a key component of digital city management in the form of secure identification of individual residents. We collect user attributes and securely transmit them to other system components for verification. Upon successful completion of the verification process, a digital identity is created for the applying resident and the set of transactions leading to the ID creation are stored in the blockchain. Our system is secure and can serve as the basis for the development of a digital infrastructure for smart city management.

The goal of intelligent transport systems (ITSs) is to enhance the network performance of vehicular ad hoc networks (VANETs). Even though it presents new opportunities to the Internet of Vehicles (IoV) environment, there are some security concerns including the need to establish trust among the connected peers. The fifth-generation (5G) communication system, which provides reliable and low-latency communication services, is seen as the technology to cater for the challenges in VANETs. The incorporation of software-defined networks (SDNs) also ensures an effective network management. However, there should be monitoring and reporting services provided in the IoV. Blockchain, which has decentralization, transparency, and immutability as some of its properties, is designed to ensure trust in networking platforms. In that regard, this article analyzes the combination of blockchain and SDN for the effective operation of the VANET systems in 5G and fog computing paradigms. With managerial responsibilities shared between the blockchain and the SDN, it helps to relieve the pressure off the controller due to the ubiquitous processing that occurs. A trust-based model that curbs malicious activities in the network is also presented. The simulation results substantially guarantee an efficient network performance, while also ensuring that there is trust among the entities.

The Aerial Internet-of-Things (Aerial-IoT) systems, deploying sensors on high-altitude platforms, e.g., drones, parachutes, and aircrafts, are a crucial monitor due to its agile maneuverability and augmentation of observation, collection, and communication. As such, the measurement accuracy and requirements of Aerial-IoT are far beyond the ability of general commercial-off-the-shelf sensors, especially in the high-altitude environment, where environmental factors (air pressure, temperature, humidity, wind movement, etc.) tend to change rapidly and lead to highly deviated readings. In this article, we tackle this challenge. First, we introduce our designed measurement system for Aerial-IoT. Then, to compensate for the low data quality and calibrate the deviation data from sensors, we take into account the inherent correlations and interaction between sensor data and environmental factors, and construct a data calibration model, called data calibration based on the neural network (DC-NN). Finally, to illustrate the effectiveness of our system, we carry out a real-world implementation by deploying sensors on the surface of parachutes in a dynamic airdrop environment. Extensive experiments on temperature-humidity-material-tensile-testing (THMTT) and high-altitude airdrop are conducted to show the significant improvements of our proposed DC-NN model.

Due to high mobility and high dynamic environments, object detection for vehicular networks is one of the most challenging tasks. However, the development of integration techniques, such as software-defined networking (SDN) and network function visualization (NFV), in networking, caching, and computing provides us with new approaches. In this article, we propose a novel context-aware object detection method based on edge-cloud cooperation. Specifically, an object detection model based on deep learning is established in the cloud server. Different from other methods, to further explore the underlying inner spatial features of collected images, the visual objects of images are regarded as nodes and the spatial relations between objects as edges, then a type of message-passing method is employed to update the nodes' features. In the mobile edge computing (MEC) servers, the context information and captured images of the vehicular environments are extracted and then are used to adjust the object detection model from the cloud server. In this way, the cloud server cooperates with the MEC servers to realize context-aware object detection, which improves the adaptation and performance of the detection model under different scenarios. The simulation results also demonstrate that the proposed method is more accurate and faster than the previous methods.

The Internet of Things (IoT) offers new opportunities for cities to make citizens live and work in more sustainable, healthy, and safe places. Since IoT applications in smart cities are characterized by different devices, networking standards, and data management strategies, trust becomes a fundamental issue in the IoT ecosystem. The explosion of IoT devices, along with their decentralized deployment, constraint resources, limited computational and cryptographic capabilities, brings challenges to trust management in IoT. The coexistence of multiple IoT domains also raises challenges, for example, how to evaluate and maintain trust across domain boundaries. This special issue aims at bringing the researchers from both academia and industry together to disseminate their recent advances related to the challenges and solutions in building trustful IoT for smart cities.

To introduce the opportunities brought by plug-in hybrid electric vehicles (PHEVs) to the energy Internet, we propose a local vehicle-to-vehicle (V2V) energy trading architecture based on fog computing in social hotspots and model the social welfare maximization (SWM) problem to balance the interests of both charging and discharging PHEVs. Considering transaction security and privacy protection issues, we employ a consortium blockchain in our designed energy trading architecture, which is different from the traditional centralized power systems, to reduce the reliance on trusted third parties. Moreover, we improve the practical Byzantine fault tolerance (PBFT) algorithm and introduce it into a consensus algorithm, called the delegated proof of stake (DPOS) algorithm, to design a more efficient and promising consensus algorithm, called DPOSP, which greatly reduces resource consumption and enhances consensus efficiency. To encourage PHEVs to participate in V2V energy transactions, we design an energy iterative bidirectional auction (EIDA) mechanism to resolve the SWM problem and obtain optimal charging and discharging decisions and energy pricing. Finally, we conduct extensive simulations to verify the proposed DPOSP algorithm and provide numerical results for a comparison with the performance of the genetic algorithm and the Lagrange algorithm in achieving EIDA.

Outsourced storage enables data owners to host their data on remote storage resources without keeping a local copy so as to target their core business. However, a serious problem is data integrity in the sense that data owners lose their physical control over the remote-stored data. Existing provable data possession protocols are overwhelmingly designed for centralized storage such as cloud, in which the server is assumed dishonest but the client is reliable. Moreover, the centralized storage suffers single-point-of-failure threat. In this paper, to deal with these issues, we propose the notion of IntegrityChain, a decentralized storage framework supporting provable data possession (PDP) based on blockchain. We formalize the system model, in which a data owner can store files to the peers in a blockchain network and check the integrity of the outsourced data periodically by paying some cryptocurrencies while the hosts can earn money if honestly provide storage service and will be punished by losing the pre-made deposit if data loss happens. In the security model, we consider the fairness in trading between a host and a data user and the soundness of the underlying decentralized PDP in this system. We come up with a concrete construction by borrowing the idea of multi-replica PDP and proof-of-retrievability and present the security analysis of the proposal. The evaluation for the construction contain two segments: the offchain part, in which we implement the algorithms locally to test the time consumption, and onchain part, in which we program a smart contract and launch it in a test network to test the gas cost for the functions.

The prosperity of cloud computing has driven an increasing number of enterprises and organizations to store their data on private or public cloud platforms. Due to the limitation of individual data owners in terms of data volume and diversity, data sharing over different cloud platforms would enable third parties to take advantage of big data analysis techniques to provide value-added services, such as providing healthcare services for customers by gathering medical data from multiple hospitals. However, it remains a challenging task to design effective incentives that encourage secure and collaborative data sharing in multiple clouds. In this paper, we propose a reliable collaboration model consisting of three types of participants, which include data owners, miners, and third parties, where the data is shared via blockchain and recorded by a smart contract. In general, these participants may acquire and store the sharing of data using their private or public clouds. We analyze the topological relationships between the participants and develop some Shapley value models from simple to complicate in the process of revenue distribution. We also discuss the incentive effect of sharing security data and rationality of the designed solution through analysis towards distribution rules.

Industrial Internet of Things (IIoT) is considered as one of the most promising revolutionary technologies to prompt smart manufacturing and increase productivity. With manufacturing being more complicated and sophisticated, an entire manufacturing process usually involves several different administrative IoT domains (e.g., factories). Devices from different domains collaborate on the same task, which raises great security and privacy concerns about device-to-device communications. Existing authentication approaches may result in heavy key management overhead or rely on a trusted third party. Thus, security and privacy issues during communication remain unsolved but imperative. In this paper, we present an efficient block-chain-assisted secure device authentication mechanism BASA for cross-domain IIoT. Specifically, consortium blockchain is introduced to construct trust among different domains. Identity-based signature (IBS) is exploited during the authentication process. To preserve the privacy of devices, we design an identity management mechanism, which can realize that devices being authenticated remain anonymous. Besides, session keys between two parties are negotiated, which can secure the subsequent communications. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed mechanism.

Cloud computing is a model for convenient, on-demand network access to virtualized environments of configurable computing resources. It is challenging to search data encrypted and stored in cloud storage servers. Searchable encryption enables data users to search on ciphertext without leaking any information about keywords and the plaintext of the data. Currently, a number of searchable encryption schemes have been proposed, but most of them provide unlimited search privileges to data users, which is not desirable in certain scenarios. In this paper, we propose a new construction of searchable encryption with fine-grained access control by using key-policy attribute-based cryptography to generate trapdoors to support AND, OR and threshold gates. The main idea is that the data owner encrypts the index keywords according to the specified access policy. The data user can generate a trapdoor to search on data, if and only if the attributes of the data user satisfy the access policy. We provide formal security proofs for the scheme, including the indistinguishability of ciphertexts and the indistinguishability of trapdoors, which are used to resist the chosen keyword attack and the keyword guessing attack of external adversaries. Comprehensive security analysis and implementation results show that the proposed scheme is provably secure and feasible in real-world applications.

Outsourcing helps relocate data from the cyber-physical system (CPS) for efficient storage at low cost. Current server-based outsourcing mainly focuses on the benefits of servers. This cannot attract users well, as their security, efficiency, and economy are not guaranteed. To solve with this issue, a hybrid outsourcing model that exploits both cloud server and edge devices to store data is needed. Meanwhile, the requirements of security and efficiency are different under specific scenarios. There is a lack of a comprehensive solution that considers all of the above issues. In this work, we overcome the above issues by proposing the first hybrid user-centric data outsourcing (HUCDO) scheme. It allows users to outsource data securely, efficiently, and economically via different CPSs. Brielly, our contributions consist of theories, implementations, and evaluations. Our theories include the first homomorphic collision-resistant chameleon hash (HCCH) and homomorphic designated-receiver signcryption (HDRS). As implementations, we instantiate how to use our proposals to outsource small- or large-scale data through distinct CPS, respectively. Additionally, a blockchain with proof-of-discrete-logarithm (B-PoDL) is instantiated to help improve our performance. Last, as demonstrated by our evaluations, our proposals are secure, efficient, and economic for users to implement while outsourcing their data via CPSs.

With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extraction method, Semantic Graph-Based Concept Extraction (SGCCE), is proposed. First, the similarities between terms are calculated using the word co-occurrence, the LDA topic model and Word2Vec. Then, a semantic graph of terms is constructed based on the similarities between the terms. Finally, according to the semantic graph of the terms, community detection algorithms are used to divide the terms into different communities where each community acts as a concept. In the experiments, we compare the concept extraction results that are obtained by different community detection algorithms to analyze the different semantic graphs. The experimental results show the effectiveness of our proposed method. This method can effectively use semantic information, and the results of the concept extraction are better from domain big data in smart cities.

The advances of artificial intelligence (AI) propels big data processing and transmission for Internet of Things (IoT), by capturing and structuring big data produced by heterogeneous devices. While applying blockchain to manage IoT devices and associated big data, the blockchain itself suffers from abuse of decentralization from anonymous users. Specifically, it has been utilized to facilitate black market trades and illegal activities. Ateniese et al. proposed using the chameleon hash (CH) to derive redactable blockchain (EuroS&P), which works by embedding a trapdoor in the basic hash function so that block content can be rewritten without causing major hard forks. In short, the redacted block hash remains unchanged. However, there is lacking intelligent design where any mistakes observed in the chain can be corrected universally and automatically. This creates disincentives to use redactable blockchain (RB) for managing big data or any data-driven business mainly due to ineffective chain redaction. To solve this problem, in this article, we propose the notion of the self-redactable blockchain (SRB) to support intelligent execution of chain redaction. Specifically, we propose the first revocable chameleon hash (RCH) to power RB. It enables an ephemeral trapdoor for finding collision without any co-operation. Periodical expiration is applied to committed hash and an ephemeral trapdoor to prevent any abuses of redaction power. We instantiate how to use our RCH to build SRB as an intelligent trust-layer for IoT. We also give a rigorous analysis as well as comprehensive experiments to validate our proposals. The evidence showed that our proposal is secure and acceptably efficient for IoT devices.

With the development of Internet of Things (IoT) and cloud technologies, numerous IoT devices and sensors transmit huge amounts of data to cloud data centers for further processing. While providing us considerable convenience, cloud-based computing and storage also bring us many security problems, such as the abuse of information collection and concentrated web servers in the cloud. Traditional intrusion detection systems and web application firewalls are becoming incompatible with the new network environment, and related systems with machine learning or deep learning are emerging. However, cloud-IoT systems increase attacks against web servers, since data centralization carries a more attractive reward. In this article, based on distributed deep learning, we propose a web attack detection system that takes advantage of analyzing URLs. The system is designed to detect web attacks and is deployed on edge devices. The cloud handles the above challenges in the paradigm of the Edge of Things. Multiple concurrent deep models are used to enhance the stability of the system and the convenience in updating. We implemented experiments on the system with two concurrent deep models and compared the system with existing systems by using several datasets. The experimental results with 99.410% in accuracy, 98.91% in true positive rate (TPR), and 99.55% in detection rate of normal requests (DRN) demonstrate the system is competitive in detecting web attacks.

Personal privacy disclosure is one of the most serious challenges in smart industrial applications. Anonymous authentication is an effective solution to protect personal privacy. However, the existing anonymous credential protocols are not perfectly suitablefor smart industrial environments such as smart vehicles in the sense that the credential revocation issue is not well-solved. In this article, we propose a Blockchain-based Anonymous authentication with Selective revocation for Smart industrial applications (BASS) for smart industrial applications supporting attribute privacy, selective revocation, credential soundness, and multishowing-unlinkability. Specifically, an efficient selective revocation mechanism is proposed based on dynamic accumulators and the signature algorithm due to Pointcheval and Sanders as the overlay of the BASS. According to the diverse demands of credential authorities, BASS can selectively provide revocation of credentials or revocation of users. We extend BASS from single-attribute privacy to multiattribute privacy as well. Finally, we implement a prototype to evaluate the cryptographic core primitives of BASS by deploying smart contracts in Ethereum to demonstrate the validity of BASS in smart industrial applications.

Due to the characteristics of urban vehicular ad hoc networks (VANETs), many difficulties exist when designing routing protocols. In this paper, we focus on designing an efficient routing strategy for vehicle-to-vehicle (V2V) communication in urban VANETs. Because, the characteristics of urban VANET routing performance are affected mainly by intersections, traffic lights, and traffic conditions, we propose an intersection-based distributed routing (IDR) strategy. In view of the fact that traffic lights are used to cause vehicles to stop at intersections, we propose an intersection vehicle fog (IVF) model, in which waiting vehicles dynamically form a collection or fog of vehicles at an intersection. Acting as infrastructure components, the IVFs proactively establish multihop links with adjacent intersections and analyze the traffic conditions on adjacent road segments using fuzzy logic. This approach offloads a large part of the routing work. During routing, the IVFs adjust the routing direction based on the real-time position of the destination, thus avoiding rerouting. Each time an IVF makes a distributed routing decision, the IDR model employs the ant colony optimization (ACO) algorithm to identify an optimal routing path whose connectivity is based on the traffic conditions existing in the multihop links between intersections. Because of the high connectivity of the routing path, the model requires only packet forwarding and not carrying when transmitting along the routing path, which reduces the transmission delay and increases the transmission ratio. The presented mathematical analyses and simulation results demonstrate that our proposed routing strategy is feasible and that it achieves relatively high performance.

Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure requiring intensive manual efforts. Worse, fresh patches tend to harm system dependability, hence deterring users from deploying them. We propose a heap patching system HEAPTHERAPY+ that simultaneously has the following prominent advantages: (1) generating patches without manual efforts; (2) installing patches without altering the code (so called code-less patching); (3) handling various heap vulnerability types; (4) imposing a very low overhead; and (5) no dependency on specific heap allocators. As a separate contribution, we propose targeted calling context encoding, which is a suite of algorithms for optimizing calling context encoding, an important technique with applications in many areas. The system properly combines heavyweight offline attack analysis with lightweight online defense generation, and provides a new countermeasure against heap attacks. The evaluation shows that the system is effective and efficient.

Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become a severe threat to the trustworthiness of machine learning. While AEs in the image domain have been well studied, audio AEs are less investigated. Recently, multiple techniques are proposed to generate audio AEs, which makes countermeasures against them urgent. Our experiments show that, given an audio AE, the transcription results by Automatic Speech Recognition (ASR) systems differ significantly (that is, poor transferability), as different ASR systems use different architectures, parameters, and training datasets. Based on this fact and inspired by Multiversion Programming, we propose a novel audio AE detection approach MVP-Ears, which utilizes the diverse off-the-shelf ASRs to determine whether an audio is an AE. We build the largest audio AE dataset to our knowledge, and the evaluation shows that the detection accuracy reaches 99.88%. While transferable audio AEs are difficult to generate at this moment, they may become a reality in future. We further adapt the idea above to proactively train the detection system for coping with transferable audio AEs. Thus, the proactive detection system is one giant step ahead of attackers working on transferable AEs.

Traditional cloud database directly stores user plaintext information, information security is directly related to the security of Cloud server, which will create a great security risk. The user's information security is not guaranteed. In this paper, a database encryption technology is designed, which can balance the problem of information security and use efficiency well. In this paper, a simplified onion encryption model is designed and implemented, which can realize the full homomorphic encryption on the cloud database to a certain extent, and improve the efficiency of ciphertext operation to a certain extent.

Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.

The Internet of Things (IoT) has attracted much attention in many fields, such as healthcare, transportation, finance and other critical infrastructures. But how to ensure the security of whole IoT system and the privacy of users has always been a challenge. Physical layer as the cornerstone of IoT, appears to be a promising direction for enhancing the security of IoT. As a consequence, we propose a novel chaotic physical layer security transmission scheme in the OFDM-based IoT transmission system. By encrypting the Discrete Fourier Transform (DFT) matrix, the proposed scheme not only provides the confidentiality of physical layer information transmission, but also effectively addresses the issues on the extreme high PAPR of the OFDM symbols. Moreover, it does not require any additional sideband information and has low computational complexity in theory. Compared with other existing schemes, the proposed scheme achieves the higher confidentiality of information transmission with the capability to resist chosen-plaintext attacks, statistical-attacks, and brute-force attacks simultaneously.

The cloud-assisted wireless body area network (WBAN) can further the development of the telemedicine, providing larger health data storage space and more powerful computing capabilities for telemedicine. Under the cloud-assisted WBAN architecture, users' health data is uploaded to a cloud server so that medical staffs can download it and analyze the users' health status. The sharing of health data between users and medical staffs provides users with better healthcare services. However, the sharing of health data introduces new privacy and security issues. To address these issues, a secure medical data sharing protocol is presented based on a new certificateless proxy re-encryption scheme without pairing. The security analysis shows that the protocol achieves the security requirements in cloud, and the performance analysis illustrates that it is more efficient than other schemes.

Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy protection. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transaction called PP-BCTS (Privacy- Preserving Blockchain Trading Scheme). It can achieve fine-grained access control through transaction arbitration in ciphertext form. This design can maximize the protection of private information and can greatly improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCTS, which can greatly improve the operational efficiency. Security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.

Security is critical to cloud services, this paper introduces a design of firewall, which based on IDS's feedback t change rules in order to detect attack flexible. It combines firewall and Intrusion Detection Systems(IDS) by using Intrusion Detection Systems, which detects ICMP, TCP, UDP attacks. Usually, a cloud service is a service built on a virtual machine. The virtual device is virtualized to achieve the purpose of multiplexing. Therefore, if you want to implement cloud security detection, you can listen to the physical device's network card. There are two types of Intrusion Detection System, one is host- based intrusion detection system(HIDS) and another is network intrusion detection system(NIDS). What's more, in order to highlight the importance of the firewall, the IDS monitoring data is analyzed and added to the firewall's defense strategy automatically. Finally, we measure the effectiveness of the system by False Negative(FN) and False Positive(FP), and verify that feedback plays a crucial role in improving the effectiveness of the system, improving the efficiency of the entire system filtering attacks.

Constrained shortest distance (CSD) queries are a valuable extension of the traditional pairwise shortest distance computation over graph-structured data, where the answers to the queries should fulfill a cost constraint (e.g., the toll payment in road networks). With the popularity of cloud computing, data owners have a strong desire to migrate their privacy-sensitive graphs to remote servers without losing the ability to query them. Existing graph encryption schemes cannot provide security guarantees for CSD queries. In this paper, we present Acro, a graph encryption scheme, which executes approximate CSD queries securely. The homomorphic encryption and the symmetric-key primitives are applied to our scheme. Through a security analysis, we prove that Acro meets the security definition of CQA2-security. The prototype of Acro is implemented and evaluated using real datasets. The results show that our proposal outperforms a state-of-the-art baseline in terms of query accuracy at the cost of enlarging query completion time.

Nowadays, The Internet of Things (IoT) has been widely used in various fields due to its smart sensing and communication capabilities. IoT devices serve as bridges for the cyber system to interact with the physical environment by providing various useful sensing capabilities such as battlefield surveillance, home monitoring, traffic control, etc. These capabilities also make IoT an important role in tactical missions in the military, including Reconnaissance, Intelligence, Surveillance, and Target Acquisition (RISTA). Nevertheless, IoT devices are known to have critical issues on security due to constraints on cost and resources. Most existing researches are based on smart sensors that have comparatively more computing and communication resources, while security solutions for dumb sensors are still lacking. Some IoT sensors that are deployed in a hostile environment are dumb due to limitations on cost and power supply, making them more vulnerable to attacks. In this work, we try to tackle this problem by proposing effective authentication solutions between a UAV and dumb IoT devices (also referred to as dumb sensors) within an example application of a UAV-sensor collaborative RISTA mission. We present two different schemes for two-way mutual authentication between the UAV and dumb sensors which utilize non-cryptographic physical layer cover channel and neighboring devices' signal sensing correlations respectively. We demonstrate the feasibility and effectiveness of our schemes with extensive real-world experiments on our prototype deployment.

In recent years, mobile racks or auto robots have been widely used in e-commerce warehouses where storage location assignment is a fundamental problem in the order picking process. The present storage location assignment strategies mainly allocate stocks into various racks according to a specific objective function or the relationships between stocks. These strategies include the random storage assignment strategy (RAS) and the good- clustering storage location assignment strategy (GCAS). In this paper, we first analyze the key factors that affect the efficiency of the order picking system.The results show that the rack- moved-number (RMN) is a significant factor in the order picking process. Then, we propose a genetic- algorithm (GA) based method for the storage location assignment problem which adopts RMN as its fitness function. To find a better solution, we take the natural deduplicated stock sequence of history orders (NDSSHO) as a seed to initialize the population of chromosomes. We also define a specific cross mutation strategy to avoid checking the validity of chromosomes by exchanging selected genes and adjusting new generated chromosomes. At last, we compare the RMN of our proposed method with RAS and GCAS. The experimental results show that the RMN of our proposed method is about 50% less than RAS and GCAS.

Cognitive radio networks (CRNs), offering novel network architecture for utilizing spectrum, have attracted significant attention in recent years. In CRNs, secondary users (SUs) first determine the status of a channel; if it is free, they start transmitting. If the status determination is wrong, SUs may unnecessarily interfere with the licensed primary user (PU). In cooperative spectrum sensing, a SU makes a decision about the presence of the PU based on its own and other SUs' sensing results. Malicious SUs (MSUs) send false sensing results to SUs so that they make wrong decisions about the PU presence. As a result, a SU may transmit during the presence of the PU or may keep starving for the spectrum. In this paper, we propose a reputation-based mechanism for cooperative spectrum sensing which can minimize the effects of MSUs on decision making. Some of the SUs are selected as distributed fusion centers (DFCs), which are responsible for making decisions about the PU presence and inform the reporting SUs. A DFC uses weighted majority voting among the reporting SUs, where weights are determined based on reputation. The DFC updates reputations of SUs based on confidence of an election. If the majority wins by a significant margin, the confidence of the election is high. In this case, SUs that belong to the majority get high reputations. We provide extensive simulations to validate our proposed model.

Internet of Things (IoT) is ubiquitous because of its broad applications and the advance in communication technologies. The capabilities of IoT also enable its important role in homeland security and tactical missions, including Reconnaissance, Intelligence, Surveillance, and Target Acquisition (RISTA). IoT security becomes the most critical issue before its extensive use in military operations. While the majority of research focuses on smart IoT devices, treatments for legacy dumb network-ready devices are lacking; moreover, IoT devices deployed in a hostile environment are often required to be dumb due to the strict hardware constraints, making them highly vulnerable to cyber attacks. To mitigate the problem, we propose a light-weight authentication scheme for dumb IoT devices, in a case study of the UAV-sensor collaborative RISTA missions. Our scheme utilizes the covert channels in the physical layer for authentications and does not request conventional key deployments, key generations which may cause security risks and large overhead that a dumb sensor cannot afford. Our scheme operates on the physical layer, and thus it is highly portable and generalizable to most commercial and military communication protocols. We demonstrate the viability of our scheme by building a prototype system and conducting experiments to emulate the behaviors of UAVs and sensors in real scenarios.

In this paper, we investigate a multi-objective optimization framework for secure wireless health monitoring applications. In particular, we consider a legitimate link for the transmission of a vital EEG signal, threatened by a passive eavesdropping attack, that aims at wiretapping these measurements. We incorporate in our framework the practical secrecy metric, namely secrecy outage probability (SOP), which requires only the knowledge of side information regarding the eavesdropper (Ev), instead of completely having its instantaneous channel state information (CSI). To that end, we formulate an optimization problem in the form of maximizing the energy efficiency of the transmitter, while minimizing the distortion encountered at the signal resulting from the compression process prior to transmission, under realistic quality of service (QoS) constraints. The problem is shown to be nonconvex and NP-complete. Towards solving the problem, a branch and bound (BnB)-based algorithm is presented where a δ-suboptimal solution, from the global optimal one, is obtained. Numerical results are conducted to verify the system performance, where it is shown that our proposed approach outperforms similar systems deploying fixed compression policies (FCPs). We successfully meet QoS requirements while optimizing the system objectives, at all channel conditions, which cannot be attained by these FCP approaches. Interestingly, we also show that a target secrecy rate can be practically achieved with nonzero probability, even when the Ev has a better channel condition, on the average, than that for the legitimate receiver.

Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous MRSE schemes constructed based on the k-nearest neighbor searchable encryption (KNN-SE) algorithm, it can mitigate some drawbacks and achieve better performance in terms of functionality and efficiency. Additionally, it does not require a predefined keyword set and support keywords in arbitrary languages. However, due to the pattern of exact matching of keywords in the new MRSE scheme, multilingual search is limited to each language and cannot be searched across languages. In this paper, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme also realizes intelligent and personalized search through flexible keyword and language preference settings. We evaluate the performance of our scheme in terms of security, functionality, precision and efficiency, via extensive experiments.

Today, Internet of Things (IoT) technology is being increasingly popular which is applied in a wide range of industry sectors such as healthcare, transportation and some critical infrastructures. With the widespread applications of IoT technology, people's lives have changed dramatically. Due to its capabilities of sensitive data-aware, information collection, communication and processing, it raises security and privacy concerns. Moreover, a malicious attacker may impersonate a legitimate user, which may cause security threat and violation privacy. In allusion to the above problems, we propose a novel and lightweight anonymous authentication and key agreement scheme for heterogeneous IoT, which is innovatively designed to shift between the public key infrastructure (PKI) and certificateless cryptography (CLC) environment. The proposed scheme not only achieves secure communication among the legal authorized users, but also possesses more attributes with user anonymity, non-repudiation and key agreement fairness. Through the security analysis, it is proved that the proposed scheme can resist replay attacks and denial of service (DOS) attacks. Finally, the performance evaluation demonstrates that our scheme is more lightweight and innovative.

Monitoring kernel object modification of virtual machine is widely used by virtual-machine-introspection-based security monitors to protect virtual machines in cloud computing, such as monitoring dentry objects to intercept file operations, etc. However, most of the current virtual machine monitors, such as KVM and Xen, only support page-level monitoring, because the Intel EPT technology can only monitor page privilege. If the out-of-virtual-machine security tools want to monitor some kernel objects, they need to intercept the operation of the whole memory page. Since there are some other objects stored in the monitored pages, the modification of them will also trigger the monitor. Therefore, page-level memory monitor usually introduces overhead to related kernel services of the target virtual machine. In this paper, we propose a low-overhead kernel object monitoring approach to reduce the overhead caused by page-level monitor. The core idea is to migrate the target kernel objects to a protected memory area and then to monitor the corresponding new memory pages. Since the new pages only contain the kernel objects to be monitored, other kernel objects will not trigger our monitor. Therefore, our monitor will not introduce runtime overhead to the related kernel service. The experimental results show that our system can monitor target kernel objects effectively only with very low overhead.

With the development of artificial intelligence and self-driving, vehicular ad-hoc network (VANET) has become an irreplaceable part of the Intelligent Transportation Systems (ITSs). However, the traditional network of the ground cannot meet the requirements of transmission, processing, and storage among vehicles. Under this circumstance, integrating space and air nodes into the whole network can provide comprehensive traffic information and reduce the transmission delay. The high mobility and low latency in the Space-Air-Ground Integrated Network (SAGIN) put forward higher requirements for security issues such as identity authentication, privacy protection and data security. This paper simplifies the Blockchain and proposes an identity authentication and privacy protection scheme based on the Hashchain in the SAGIN. The scheme focuses on the characteristics of the wireless signal to identify and authenticate the nodes. The verification and backup of the records on the block are implemented with the distributed streaming platform, Kafka algorithm, instead of the consensus. Furthermore, this paper analyzes the security of this scheme. Afterward, the experimental results reveal the delay brought by the scheme using the simulation of SUMO, OMNeT++, and Veins.

With the prevalence of blockchain, more and more Decentralized Applications (DApps) are deployed on Ethereum to achieve the goal of communicating without supervision. Users habits may be leaked while these applications adopt SSL/TLS to encrypt their transmission data. Encrypted protocol and the same blockchain platform bring challenges to the traffic classification of DApps. Existing encrypted traffic classification methods suffer from low accuracy in the situation of DApps.

Summary Fog computing, a non‐trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine‐grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customer's daily activities. Non‐intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy‐preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade‐off between utility and privacy compared with other popular methods.

Biometric recognition and analysis are among the most trusted features to be used by Implantable Medical Devices (IMDs). We aim to secure these devices by using these features in emergency scenarios. As patients can witness unpredictable lethal accidents, any implantable medical device should allow access to urgent medical interventions from legitimate parties. Any delay in providing immediate medical support can endanger the patient’s life. Hence, we propose in this work an authentication scheme that allows access to the implanted devices in emergency situations for only legitimate users. We have designed in the first place a scheme for authentication using Electrocardiogram instantaneous readings. Then, we joined the latter to a fixed biometric reading, which is fingerprint reading, to enable access to emergency medical teams. We have designed a scheme in a way to prevent attackers from accessing/hijacking the device even during emergency situations . This scheme has been assisted with elliptic curve cryptography to protect the wireless exchange of requested keys. The scheme relies on the instantaneous reading of the patient’s heartbeat and his/her fingerprint reading to create a secure key. This key will validate the authentication request of the new medical team. We have analyzed this scheme deeply to verify that they offer the necessary security for the patient’s life. We have tested if the wireless exchange of the key will expose the device’s privacy. We have also tested the accuracy of the authentication process to ensure a safe and a valid performance of the authentication process . The scheme has been designed with consideration to any hardware/software limitation that characterize any implantable medical device.

Recently, with the rapid development of big data, Internet of Things (IoT) brings more and more intelligent and convenient services to people's daily lives. Mobile healthcare crowd sensing (MHCS), as a typical application of IoT, is becoming an effective approach to provide various medical and healthcare services to individual or organizations. However, MHCS still have to face to different security challenges in practice. For example, how to quickly and effectively authenticate masses of bio-information uploaded by IoT terminals without revealing the owners' sensitive information. Therefore, we propose a large-scale concurrent data anonymous batch verification scheme for MHCS based on an improved certificateless aggregate signature. The proposed scheme can authenticate all sensing bio-information at once in a privacy preserving way. The individual data generated by different users can be verified in batch, while the actual identity of participants is hidden. Moreover, assuming the intractability of computational Diffie-Hellman problem, our scheme is proved to be secure. Finally, the performance evaluation shows that the proposed scheme is suitable for MHCS, due to its high efficiency.

Phrase search allows retrieval of documents containing an exact phrase, which plays an important role in many machine learning applications for cloud-based Internet of Things (IoT), such as intelligent medical data analytics. In order to protect sensitive information from being leaked by service providers, documents (e.g., clinic records) are usually encrypted by data owners before being outsourced to the cloud. This, however, makes the search operation an extremely challenging task. Existing searchable encryption schemes for multikeyword search operations fail to perform phrase search, as they are unable to determine the location relationship of multiple keywords in a queried phrase over encrypted data on the cloud server side. In this paper, we propose P3, an efficient privacy-preserving phrase search scheme for intelligent encrypted data processing in cloud-based IoT. Our scheme exploits the homomorphic encryption and bilinear map to determine the location relationship of multiple queried keywords over encrypted data. It also utilizes a probabilistic trapdoor generation algorithm to protect users' search patterns. Thorough security analysis demonstrates the security guarantees achieved by P3. We implement a prototype and conduct extensive experiments on real-world datasets. The evaluation results show that compared with existing multikeyword search schemes, P3 can greatly improve the search accuracy with moderate overheads.

Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labeled IoT data from multiple entities, raising great concerns about data privacy. Most of the existing solutions rely on an implicit assumption that the training data can be reliably collected from multiple data providers, which is often not the case in reality. To bridge the gap between ideal assumptions and realistic constraints, in this paper, we propose secureSVM, which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. We utilize the blockchain techniques to build a secure and reliable data sharing platform among multiple data providers, where IoT data is encrypted and then recorded on a distributed ledger. We design secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and construct a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party. Rigorous security analysis prove that the proposed scheme ensures the confidentiality of the sensitive data for each data provider as well as the SVM model parameters for data analysts. Extensive experiments demonstrates the efficiency of the proposed scheme.

The wireless body area network (WBAN) has attracted considerable attention. Two main problems exist in WBANs: 1) the quality of service (QoS) requirements and 2) the energy efficiency of data transmission. To solve the above problems, in this paper, we carefully design the sensor priority according to the importance degree, sampling rate, timeout condition, and remaining energy. Then, considering the priority of the node and the channel factors, a utility function is introduced to characterize the value of a node transmitting data frames in a specific time period. Next, we model the time slot allocation problem, where the objective is to maximize the total utility of the data transmission of all nodes in a specified period of time by adjusting the transmission time and the transmission duration of each node. Finally, according to the problem model, we propose a time slot allocation scheme based on a greedy strategy, which effectively reduces the time complexity of the direct solution to the problem. In this scheme, nodes with higher priority are arranged to transmit data frames in the time slots with better channel conditions. The experimental results show that the proposed scheme achieves substantial improvements in QoS and energy efficiency relative to the comparison schemes.

A large number of plug-in hybrid electric vehicles (PHEVs) have high mobility but a small battery capacity; thus, these vehicles urgently need to make charging and discharging decisions in real time. This paper proposes a new architecture based on fog computing for an Internet of Vehicles energy trading system, which we call a vehicle-mounted energy fog. This architecture includes a fog computing energy center (FCEC), which manages local energy trading and reduces the peak load energy trading for an external public energy company. We model the optimization problems for energy trading under two different types of FCECs: 1) a nonprofit-driven FCEC whose goal is solely to benefit the PHEV charging and discharging operations and 2) a profit-driven FCEC whose goal is to maximize its own profits while still guaranteeing that each PHEV achieves a non-negative utility. We also propose efficient algorithms for these two types of FCECs to seek optimal pricing and make supply demand decisions. Simulation results show that our proposed algorithms are superior to existing algorithms in terms of the convergence rate, the final objective value and the evenness of the Pareto solution set. Specifically, the evenness of the Pareto solution set is improved by 23% compared to the results of the existing algorithm.

Nowadays in many application scenarios of Internet of Things (IoT), low latency is achieved at the cost of computing-complexity which is beyond the capabilities of IoT devices. Offloading the computing intensive tasks to more powerful edge devices is expected to provide new generation computing-intensive and delay-sensitive services. In the three hierarchy architecture user/IoT-edge-cloud, private and secure mutual authentication are necessary between user, IoT device, and edge device. However, in the emerging computing paradigms, such as mobile transparent computing, edge computing, fog computing, and several threats, such as edge device compromise, privacy leaking, and denial of service (DoS) might crash the security of the system. Here, we propose a lightweight anonymous mutual authentication scheme for ${n}$ -times computing offloading (CO) in IoT. In our novel scheme, through a smartcard as token and an edge device as a security proxy, a user is able to subscribe or renew ${n}$ -times CO service and consume it securely in daily use. Moreover, both IoT and edge devices authenticate each other anonymously without leaking user's sensitive information, which will preserve the privacy even when an edge device is comprised. Finally, our scheme is based on lightweight one-way hash function and MAC function, therefore the adversary is not able to perform a DoS attack. To evaluate the solution, a security analysis and a performance analysis are presented. Compared with similar schemes, our approach achieves all designed security features and achieves a $1.66\boldsymbol {\times }$ and $2.87\boldsymbol {\times }$ of computing speed on IoT and edge devices, respectably.

With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.

Wireless body area networks (WBANs) are often used to provide communication services for the data from the body. Since the data in WBANs are always closely related to individuals, they need to be kept secret with integrity. Key management is critical to data security. The resource-constraint biosensors make it difficult for traditional key management mechanisms to work well in WBAN. Using physiological signals to realize key agreement has the advantages of low overhead, timely key updating, and no key material predeployment and key store requirements, etc. The existing physiological signal-based key agreement schemes are always unable to balance the overhead and security well. To overcome these problems, we make two efforts. One is that we try to enhance the randomness of the interpulse-interval (IPI) from electrocardiograms in the process of digitizing physiological signals. And the other is that we attempt to use the Bloom filter rather than lots of chaff points to conceal the features exchanged for key agreement. The comparative analysis and experiments indicate that the proposed scheme can simultaneously achieve high security strength and low overhead.

Currently, the number of Internet of Things (IoT) devices making up the IoT is more than 11 billion and this number has been continuously increasing. The prevalence of these devices leads to an emerging IoT business model called Device-as-a-service, which enables sensor devices to collect data disseminated to all interested devices. The devices sharing data with other devices could receive some financial reward, such as Bitcoin. However, side-channel attacks, which aim to exploit some information leaked from the IoT devices during data trade execution, are possible since most of the IoT devices are vulnerable to be hacked or compromised. Thus, it is challenging to securely realize data trading in IoT environment due to the information leakage, such as leaking the private key for signing a Bitcoin transaction in Bitcoin system. In this paper, we propose LRCoin, a kind of leakage-resilient cryptocurrency based on bitcoin in which the signature algorithm used for authenticating bitcoin transactions is leakage-resilient. LRCoin is suitable for the scenarios where information leakage is inevitable, such as IoT applications. Our core contribution is proposing an efficient bilinear-based continual-leakage-resilient ECDSA signature. We prove the proposed signature algorithm is unforgeable against adaptively chosen messages attack in the generic bilinear group model under the continual leakage setting. Both the theoretical analysis and the implementation demonstrate the practicability of the proposed scheme.

Network function virtualization (NFV) is a new way to provide services to users in a network. Different from dedicated hardware that realizes the network functions for an IoT application, the network function of an NFV network is executed on general servers, and in order to achieve complete network functions, service function chaining (SFC) chains virtual network functions to work together to support an IoT application. In this paper, we focus on a main challenge in this domain, i.e., resource efficient provisioning for social IoT application oriented SFC requests. We propose an online SFC deployment algorithm based on the layered strategies of physical networks and an evaluation of physical network nodes, which can efficiently reduce bandwidth resource consumption (OSFCD-LSEM) and support the security and privacy of social IoT applications. The results of our simulation show that our proposed algorithm improves the bandwidth carrying rate, time efficiency, and acceptance rate by 50%, 60%, and 15%, respectively.

Applying consortium blockchain as a trust layer for heterogeneous industrial Internet-of-Things devices is cost-effective. However, with an increase in computing power, some powerful attacks (e.g., the 51% attack) are inevitable and will cause severe consequences. Recent studies also confirm that anonymity and immutability of blockchain have been abused to facilitate black market trades, etc. To operate controllable blockchain for IIoT devices, it is necessary to rewrite blockchain history back to a normal state once the chain is breached. Ateniese et al. proposed redactable blockchain by using chameleon hash (CH) to replace traditional hash function, it allows blockchain history to be written when needed (EuroS&P 2017). However, we cannot apply this idea directly to IIoT without solving the following problems: (1) achieve a decentralized design of CH; (2) update the signatures accordingly to authenticate the redacted contents; (3) satisfy the low-computing need of the individual IIoT device. In this paper, we overcome the above issues by proposing the first threshold chameleon hash (TCH) and accountable-and-sanitizable chameleon signature (ASCS) schemes. Based on them, we build a redactable consortium blockchain which is efficient for IIoT devices to operate. It allows a group of authorized sensors to write and rewrite blockchain without causing any hard forks. Basically, TCH is the first TCH and ASCS is a public-key signature supporting file-level and block-level modifications of signatures without impairing authentications. Additionally, ASCS achieves accountability to avoid abuse of redaction. While security analysis validates our proposals, the simulation results show that redaction is acceptably efficient if it is executed at a small scale or if we adopt a coarse-grained redaction while sacrificing some securities.

Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

Application repackaging is a severe threat to Android users and the market. Existing countermeasures mostly detect repackaging based on app similarity measurement and rely on a central party to perform detection, which is unscalable and imprecise. We instead consider building the detection capability into apps, such that user devices are made use of to detect repackaging in a decentralized fashion. The main challenge is how to protect repackaging detection code from attacks. We propose a creative use of logic bombs, which are regularly used in malware, to conquer the challenge. A novel bomb structure is invented and used: the trigger conditions are constructed to exploit the differences between the attacker and users, such that a bomb that lies dormant on the attacker side will be activated on one of the user devices, while the repackaging detection code, which is packed as the bomb payload, is kept inactive until the trigger conditions are satisfied. Moreover, the repackaging detection code is woven into the original app code and gets encrypted; thus, attacks by modifying or deleting suspicious code will corrupt the app itself. We have implemented a prototype, named BombDroid, that builds the repackaging detection into apps through bytecode instrumentation, and the evaluation shows that the technique is effective, efficient, and resilient to various adversary analysis including symbol execution, multi-path exploration, and program slicing.

Nowadays, Implantable Medical Devices (IMDs) rely mainly on wireless technology for information exchange. In spite of the many advantages wireless technology offers to patients in terms of efficiency, speed and ease; it puts the patients' health in serious danger if no proper security mechanism is deployed. The IMDs rely generally on resources that are relatively simple and sometimes require surgery to be altered. Therefore, common security mechanisms cannot be simply implemented in fear of consuming all the resources held for healthcare purposes. A certain balance between security and efficiency must be found in each IMD architecture. In this work, we try to avoid encryption algorithms to protect IMDs from Man-In-The-Middle (MITM) attacks. Encryption is generally used to protect communication confidentiality. However, this method is still a subject for replay and MITM attacks. In this work, we propose to create a signature protocol that protects IMDs from MITM attempts using less resources than common encryption/decryption algorithms. This signature algorithm is dynamic, which means that the signature output depends on a key and the same message can have different signatures if this key is different. This dynamic part will be introduced using chaotic generators.

The cognitive network system learns from the past (situations, plans, decisions, actions) and uses this knowledge to improve the decisions in the future. Scenarios in which data resources for configuring radio-system parameters are stored in the cloud for easily sharing and exchanging between nodes are foreseeable. Due to the physical inaccessibility and limited control, it is always a tough topic to formulate appropriate access control strategies for the cloud data and data access requests submitted by applications are not always correct and credible. Cloud servers cannot clearly confirm that these requests are consistent with a user's original intent. In this paper, we propose a new access control method and forensic framework for user-intent monitoring of cloud-based networked applications in cognitive networks. Our framework has two main functions. Firstly, it makes sure that every data access request submitted by applications is correct. This means that it accurately shows what it wants. Monitoring user-intent can also help the cognitive engine to make decisions in turn. Secondly, it can offer adequate details to help forensic analysts reconstruct a precise view of user interaction with applications and understand system conditions. Our framework can function correctly in untrusted environments and is transparent to applications, systems and communication environments. It incurs no discernible performance overhead.

Vehicular Ad Hoc Networks (VANETs) are attractive scenarios that can improve the traffic situation and provide convenient services for drivers and passengers via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, there are still many security challenges in the traffic information transmission, especially in the intense traffic case. For ensuring the privacy of users and traceability of vehicles, we propose a traceable concurrent data anonymous transmission scheme for heterogeneous VANETs. The scheme is based on certificateless aggregate signcryption, so it supports batch verification. Moreover, conditional anonymity is also achieved due to the involving of the pseudo-ID technique. Furthermore, it is a pairing-free scheme for the merit of multi-trapdoor hash functions. As a result, the total computation overhead is greatly reduced.

Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.

In recent years, cloud storage technology has been widely used in many fields such as education, business, medical and more because of its convenience and low cost. With the widespread applications of cloud storage technology, data access control methods become more and more important in cloud-based network. The ciphertext policy attribute-based encryption (CP-ABE) scheme is very suitable for access control of data in cloud storage. However, in many practical scenarios, all attributes of a user cannot be managed by one authority, so many multi-authority CP-ABE schemes have emerged. Moreover, cloud servers are usually semi-trusted, which may leak user information. Aiming at the above problems, we propose a fine-grained access control scheme with versatility for cloud storage based on multi-authority CP-ABE, named vFAC. The proposed vFAC has the features of large universe, no key escrow problem, online/offline mechanism, hidden policy, verifiability and user revocation. Finally, we demonstrate vFAC is static security under the random oracle model. Through the comparison of several existing schemes in terms of features, computational overhead and storage cost, we can draw a conclusion that vFAC is more comprehensive and scalable.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

The multipath transmission scheme in vehicular networks has become a hot topic. It is a great challenge to overcome the unreliability of wireless network in multipath transmission. Recently, scholars propose a lot of network coding schemes to solve this problem. These schemes implement network coding algorithms by bitwise X O R or Galois Field arithmetic. However, these schemes cannot take into account both coding flexibility and computational complexity. Therefore, we propose a BigNum Network Coding (BNNC) scheme. The core idea of the BNNC scheme is to treat a packet as an integer and implement the network coding through linear operations on the integer set. It replaces bitwise XOR and Galois Field arithmetic with integer arithmetic that guarantees high coding flexibility and low computational complexity. In this paper, first, we propose the BNN C scheme that can effectively improve the reliability of multipath transmission in vehicular networks with lower computational complexity than current network coding scheme. Second, we design the Independent Matrix that enables the coding process to improve coding efficiency without independent check. Third, we compare BNNC scheme with Earliest Completion First (ECF) and Galois Field network coding scheme through a lot of simulations and real tests. The results show that the BNN C scheme is significantly superior to the Galois Field network coding schemes in terms of computational performance. And in terms of the network performance, the BNNC scheme can overcome the unreliability of links in multipath transmission.

More data and applications are moving to the cloud, which presents many new security risks. Malware is one of the most significant threats to cloud computing. In this paper, we explore to employ virtual machine introspection(VMI) and memory forensics analysis(MFA) techniques to detect malware running in guest virtual machines. Our scheme differs from existing malware detection methods based on virtualization technology in three aspects. First, this paper combines VMI with MFA to extract multiple type features in the guest virtual machine at the same time. Our scheme can effectively minimize the data acquisition overhead. Second,compared with single dynamic feature or multiple static feature detection methods, our data acquisition method employs dynamic multiple type features, and effectively promotes the ability of sophisticated malware detection. Finally,we use AdaBoost ensemble learning method and combination strategy of voting to improve the accuracy and generalization ability of the overall classifier. The experimental results based on a lot of real-world malware show that our scheme can achieve a detection accuracy of 0.9975. Our approach can improve virtual machines security, and further effectively enhance the security of cloud computing environment.

Beyond their benign uses, the small Unmanned Aerial Vehicles (UAVs) are expected to take the major role in future smart cities that have attracted the attention of the public and authorities. Therefore, detecting, tracking and classifying the type of UAVs is important for surveillance and air traffic management applications. Existing UAVs detection works focus on radars, visual detection, and acoustic sensors. However, the work was done by applying Support Vector Machine (SVM), k-Nearest Neighbor (KNN) based methods to classify the UAVs need a large number of samples for feature extraction to train a model. In this paper, we propose a new small UAVs classification system using Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs) based on the wireless signals collected from the UAVs of various types. Before the classification, using the Universal Software Radio Peripheral (USRP), oscilloscope and antenna to collect the wireless signals, preprocessing and dimensionality reduction to represent information at a lower dimension space. The processed data from UAVs is input to the UAVs' discriminant model of the AC-WGANs for classification. The obtained results show the effectiveness of the proposed system, which can achieve a recognition accuracy of around 95% in the indoor environment and can also be suitable in the outdoor environment.

Due to the fast increase of mobile traffic, most mobile network operators face the congestion issue in licensed spectrum bands. Several telecommunication vendors and operators propose to expand LTE service to the unlicensed spectrum bands to relieve the traffic congestion. However, LTE in unlicensed spectrum may interfere with Wi-Fi communications in the same bands and cause significant decrease in the quality of service of Wi-Fi. In this paper, we propose a novel mechanism that enables negotiations between two different wireless technologies (Wi-Fi and LTE), which ensures fair spectrum sharing between Wi-Fi and LTE-Unlicensed (LTE-U) in the same bands. We formulate the co-existence of Wi-Fi and LTE-U as a constrained optimization problem, and we solve the problem. We evaluate the performance of the proposed scheme via NS-3 simulations. The simulation results show that our approach can effectively improve the overall channel utilization and reduce the interference between Wi-Fi and LTE-U.

Internet of Vehicles (IoV) is an intelligent application of IoT in smart transportation, which can make intelligent decisions for passengers. It has drawn extensive attention to improve traffic safety and efficiency and create a more comfortable driving and riding environment. Vehicular cloud computing is a variant of mobile cloud computing, which can process local information quickly. The cooperation of the Internet and vehicular cloud can make the communication more efficient in IoV. In this paper, we mainly focus on the secure communication between vehicles and roadside units. We first propose a new certificateless short signature scheme (CLSS) and prove the unforgeability of it in random oracle model. Then, by combining CLSS and a regional management strategy we design an efficient anonymous mutual quick authentication scheme for IoV. Additionally, the quantitative performance analysis shows that the proposed scheme achieves higher efficiency in terms of interaction between vehicles and roadside units compared with other existing schemes.

Along with the development of wireless communication technology, a mass of mobile devices are gaining stronger sensing capability, which brings a novel paradigm to light: participatory sensing networks (PSNs). PSNs can greatly reduce the cost of wireless sensor networks, and hence are becoming an efficient way to obtain abundant sensing data from surrounding environment. Therefore, PSNs would lead to significant improvement in various fields, including cognitive communication. However, the large-scale deployment of participatory sensing applications is hindered by the lack of incentive mechanism, security and privacy concerns. It is still an ongoing issue to address all three aspects simultaneously in PSNs. In this paper, we construct an efficient privacy-preserving incentive scheme without trusted third party (TTP) for PSNs to motivate user-participation. This scheme allows each participant to earn credits by contributing data privately. Using blind and partially blind signatures, the proposed scheme is proved to be secure for privacy and incentive. Additionally, the performance evaluation in terms of computation and storage indicates that the proposed scheme has higher efficiency.

Smart personal insulin pumps have been widely adopted by type 1 diabetes. However, many wireless insulin pump systems lack security mechanisms to protect them from malicious attacks. In previous works, the read-write attacks over RF channels can be launched stealthily and could jeopardize patients' lives. Protecting patients from such attacks is urgent. To address this issue, we propose a novel visible light channel based access control scheme for wireless infusion insulin pumps. This scheme employs an infrared photodiode sensor as a receiver in an insulin pump, and an infrared LED as an emitter in a doctor's reader (USB) to transmit a PIN/shared key to authenticate the doctor's USB. The evaluation results demonstrate that our scheme can reliably pass the authentication process with a low false accept rate (0.05% at a distance of 5cm).

Many large-scale compute-intensive and mission-critical online service applications are being deployed on geo-distributed datacenters, which require transfers of bulk business data over Wide Area Networks (WANs). The bulk transfers are often associated with different requirements on deadlines, either a complete transfer before a hard deadline or a best-effort delivery within a soft deadline. In this paper, we study the online bulk transfer problem over inter-datacenter WANs, while taking into consideration the requests with a mixture of hard and soft deadlines. We use Linear Programming (LP) to mathematically formulate the problem with the objective of maximizing a system utility represented by the service provider's revenue, taking into account the revenue earned from deadline-met transfers and the penalty paid for deadline-missed ones. We propose an online framework to efficiently manage mixed bulk transfers and design a competitive algorithm that applies the primal-dual method to make routing and resource allocation based on the LP. We perform theoretical analysis to prove that the proposed approach can achieve a competitive ratio of (e-1)/e with little link capacity augmentation. In addition, we conduct comprehensive simulations to evaluate the performance of our method. Simulation results show that our method irrespective of the revenue model, can accept at least 25% more transfer requests and improve the network utilization by at least 35%, compared to prior solutions.

To solve the issue of scarce spectrum resources of the existing cellular network, LTE-U that expands LTE service to the unlicensed 5GHz spectrum is proposed. However, the centralized medium access control protocol of LTE largely decreases the performance of Wi-Fi networks operating in the same unlicensed spectrum. In the paper, to solve the problem, we propose a new mechanism based on the duty-cycle method. It can adaptively adjust the percentage of the airtime used by a LTE Small cell Base Station (SBS) according to the bandwidth of the licensed spectrum of the SBS and downlink data rate demands of the SBS users to maximize throughput of the SBS network on the unlicensed spectrum while ensuring fairness between the Wi-Fi and SBS network. The fairness is based on 3GPP proposed fairness coexistence criterion. To ensure the fairness, we propose a method to construct a W-Fi network offering the same level of the SBS traffic load, and throughput maximization of the SBS is formulated as a constrained non-linear optimization problem solved by an optimal algorithm. We evaluate the proposed mechanism from two aspects. The first is to prove the proposed Wi-Fi network construction method is valid. The second is to evaluate the performance of our proposed method. Simulation results show that our approach is valid and it can maximize the throughput of the SBS network and ensure the fairness criterion.

The combination of an appified smart home platform and third-party apps have enabled developers to contribute their novel ideas to bring more convenience to their users. However, this also brings the potential of privacy leakage. If a third-party app is permitted to monitor a user day and night, then it will learn the behavior pattern of this user before long. In this paper, we exploited how IFTTT monitors the daily life of a user in several ways that are hardly noticeable. We propose the “Specific-fuzzification” to protect the privacy of a user in two steps: filter the unnecessary events to the IFTTT, then fuzz the value of the events that must be uploaded. We evaluated the “Specific-fuzzification” on event records of seven users, the result showed comparing the original IFTTT, the modified IFTTT patched with “Specific-fuzzification” only gained rare events and thus could no longer recognize any behavior patterns of a user.

Secure communication in medical devices is a pillar in ensuring patient's safety. However, in emergency cases, this can hinder the recovery of the patient. If an emergency team cannot give themselves access to the IMD without the user's assistance, they may be unable to offer any help. This paper introduces a security scheme for similar cases. By creating a backdoor to the IMDs, legal authentication may be performed with the IMD and gain access to it. This work presents a procedure for an emergency team to validate their actions to the IMD without the need of the patient's conscious. This is ensured using hashing function and elliptic curves for the security key generation. The seed that will be used will be the heart rhythm of the patient. The authentication process introduced will only allow access to the identified parties. An eavesdropper will be unable to interfere during emergency cases and can threaten patients' lives.

Wireless medical devices play an important role in providing safety and privacy to patients suffering from major health issues. These light-weight devices can be worn inside or outside the patient's body and provide more convenience and reliable doctor-patient communication. However, the design, development, and usage of these devices play a critical role in present network paradigm. They are vulnerable to network threats and attacks which break the confidentiality, integrity and availability protocols in networking scenarios. Thus, it is important to have identification and authentication of only the authorized peoplewho can operate the device. This paper proposes Dynamic Time Warping (DTW) algorithm for providing trusted authentication and identification of only authorized people using ECG signal. Here, DTW algorithm is used to measure the correlation between different ECG signal records. Experiments were carried out to evaluate the proposed algorithm with a large database consisting of users of al1 ages, including abnormal ECG data and long span of time intervals between ECG recordings for evaluating the reliability of the proposed algorithm. Comparative evaluation of the proposed sy stem show ed that, it is not only efficient, but also light weight in comparison to the existing systems.

Insulin pumps have been widely used by patients with diabetes. Insulin pump systems adopt wireless channel with few cryptographic mechanisms, which makes them vulnerable to many attacks. In this paper, we focus on the wireless channel between Carelink USB and insulin pump on which the attackers can launch message eavesdropping and/or therapy manipulation attacks, which may put the patient in a life-threatening situation. Some prior solutions such as certificate-based or token-based schemes need either complicated key management or additional devices. We propose a novel voiceprint-based access control scheme comprising anti-replay speaker verification and voiceprint-based key agreement to secure the channel between the Carelink USB and insulin pump. Our scheme does not need permanent key sharing or additional devices. The anti-replay speaker verification adopts cascaded fusion of speaker verification and anti-replay countermeasure to ensure the insulin pump can be accessed by Carelink USB only after the legitimate user passes the identity verification. The evaluation on ASVspoof 2017 datasets shows that our scheme achieves a 4.02% Equal Error Rate (EER) with the existence of replay impostors. Besides, our scheme uses energy-difference-based voiceprint extraction and secure multi-party computing to generate a common cryptography (temporary) key between the Carelink USB and insulin pump, which can be used to encrypt the subsequent communication, and protect the insulin pump from eavesdropping and therapy manipulation attacks. By appropriately setting the similarity threshold of voiceprints, our key agreement scheme allows the insulin pump to establish a secure channel only with the device in its close proximity.

Space wireless networks composed of a large number of low-cost miniaturized nanosatellites enable many promising astronautical applications such as the distributed and cooperative sensing. Data collection via multihop relay is critical for nanosatellite networks. However, due to the small size of onboard solar panels and limited attitude control, energy harvesting (EH) capability of nanosatellites is quite low and unstable. In addition, the space networks have 3-D topologies due to various orbit altitudes. In this paper, we propose a novel 3-D EH-aware routing (3DEHR) scheme for nanosatellite networks. The main idea is to establish the 3-D artificial potential field (APF) based on the EH capabilities and geographic positions of neighboring nanosatellites. A nanosatellite node determines the next hop among its neighbors based on the composite force direction obtained from the APF for data forwarding. We also design the methods in 3DEHR to solve the routing void problem and to circumvent sensitive nodes such as malicious satellites. Simulation results show that 3DEHR outperforms the existing routing schemes in hop stretch and packet delivery rate. Since the routing paths are planned based on the EH capabilities of nanosatellites, the harvested energy in the network is fully utilized and the network lifetime is extended. 3DEHR is suitable for nanosatellite networks in space and other 3-D ad hoc networks with dynamic EH capabilities and topologies.

Recent advances in socially aware networks (SANs) have allowed its use in many domains, out of which the Social Internet of Vehicles (SIOV) is of prime importance. SANs can provide a promising routing and forwarding paradigm for SIOV by using interest-based communication. Though able to improve the forwarding performance, existing interest-based schemes fail to consider the important issue of protecting users' interest information. In this paper, we propose a privacy-preserving interest-based forwarding scheme (PRIF) for SIOV, which not only protects the interest information but also improves the forwarding performance. We propose a privacy-preserving authentication protocol to recognize communities among mobile nodes. During data routing and forwarding, a node can know others' interests only if they are affiliated with the same community. Moreover, to improve forwarding performance, a new metric community energy is introduced to indicate vehicular social proximity. Community energy is generated when two nodes encounter one another and information is shared among them. PRIF considers this energy metric to select forwarders toward the destination node or the destination community. Security analysis indicates PRIF can protect nodes' interest information. In addition, extensive simulations have been conducted to demonstrate that PRIF outperforms the existing algorithms, including the BEEINFO, Epidemic, and PRoPHET.

Reasonable design and effective detection of the random access preamble has become a challenging task due to the unique characteristics of mobile satellite communications. To tackle this challenge, we first design a universal long sequence structure by concatenating multiple short Zadoff-Chu sequences that are insensitive to carrier frequency offset (CFO), and then propose the new principles of parameter selection for short sequences to ensure the minimum utilization of root sequence and the independence of the cyclic shift offset on the beam radius. To further reduce the detection complexity and improve the multi-user access performance, a fast timing detection approach is also presented by leveraging the piecewise cumulative detection and the multi-peaks joint estimation to obtain an accurate timing advance for each access user. Simulation results and complexity analysis validate the effectiveness of the new preamble in a typical satellite communication environment, and reveal that the proposed timing detection can achieve the robustness to CFO and offer outstanding performance improvements especially in multi-user scenarios while having a notably reduced computational complexity.

Existing distributed denial-of-service attack detection in software defined networks (SDNs) typically perform detection in a single domain. In reality, abnormal traffic usually affects multiple network domains. Thus, a cross-domain attack detection has been proposed to improve detection performance. However, when participating in detection, the domain of each SDN needs to provide a large amount of real traffic data, from which private information may be leaked. Existing multiparty privacy protection schemes often achieve privacy guarantees by sacrificing accuracy or increasing the time cost. Achieving both high accuracy and reasonable time consumption is a challenging task. In this paper, we propose Predis, which is a privacy-preserving cross-domain attack detection scheme for SDNs. Predis combines perturbation encryption and data encryption to protect privacy and employs a computationally simple and efficient algorithm k-Nearest Neighbors (kNN) as its detection algorithm. We also improve kNN to achieve better efficiency. Via theoretical analysis and extensive simulations, we demonstrate that Predis is capable of achieving efficient and accurate attack detection while securing sensitive information of each domain.

Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. However, the development is hampered by data privacy concerns: data owners no longer physically possess the storage of their data. In this work, we study the issue of privacy-preserving set-valued data publishing. Existing data privacy-preserving techniques (such as encryption, suppression, generalization) are not applicable in many real scenes, since they would incur large overhead for data query or high information loss. Motivated by this observation, we present a suite of new techniques that make privacy-aware set-valued data publishing feasible on hybrid cloud. On data publishing phase, we propose a data partition technique, named extended quasi-identifierpartitioning (EQI-partitioning), which disassociates record terms that participate in identifying combinations. This way the cloud server cannot associate with high probability a record with rare term combinations. We prove the privacy guarantee of our mechanism. On data querying phase, we adopt interactive differential privacy strategy to resist privacy breaches from statistical queries. We finally evaluate its performance using real-life data sets on our cloud test-bed. Our extensive experiments demonstrate the validity and practicality of the proposed scheme.

Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world data sets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme.

The advances of cloud computing, fog computing, and Internet of things (IoT) make industries more prosperous than ever. A wide range of industrial systems such as transportation and manufacturing systems have been developed by integrating cloud computing, fog computing, and IoT infrastructure successfully. However, in this sophisticated system, security and privacy issues are major concerns that hinder the widespread adoptions of these novel techniques. In this paper, we focus on assured data deletion, an issue that is important but received less attention in academia and industry. We first propose a framework to integrate the cloud, the fog, and the things together to manage stored data from industries or individuals. We then focus on secure data deletion in this framework by proposing an assured data deletion scheme that fulfills verifiable data deletion as well as flexible access control over sensitive data. Only data owners and fog devices are involved when deleting cloud data and validating the deletion of these data, which makes the protocol practical due to the features of low latency as well as real-time interaction with fog. The proposed protocol takes advantage of the attribute-based encryption, whose security can be proved under the standard model. The theoretical analysis shows good performance and functionality requirements while the implementation results demonstrate the feasibility of our proposal.

Cloud storage has been used to reduce the cost and support convenient collaborations for industrial Internet of things (IIoT) data management. When data owners share IIoT data with authorized parties for data interaction, secure cloud data searching and file access control are fundamental security requirements. In this paper, first we discuss a new insider attack to the Cui's multi-key aggregate searchable encryption scheme, where the unauthorized inside users can guess the other users private keys. Then, we propose a novel file-centric multi-key aggregate keyword searchable encryption (Fc-MKA-KSE) system for the IIoT data in the file-centric framework. Specifically, we present two formal security models, namely, the security models of the indistinguishable selective-file chosen keyword attack and the indistinguishable selective-file keyword guessing attack, which can satisfy the security requirements. Our experimental results show that the proposed scheme achieves computational efficiency.

Vehicle location prediction or vehicle tracking is a significant topic within connected vehicles. This task, however, is difficult if merely a single modal data is available, probably causing biases and impeding the accuracy. With the development of sensor networks in connected vehicles, multimodal data are becoming accessible. Therefore, we propose a framework for vehicle tracking with multimodal data fusion. Specifically, we fuse the results of two modalities, images and velocities, in our vehicle-tracking task. Images, being processed in the module of vehicle detection, provide visual information about the features of vehicles, whereas velocity estimation can further evaluate the possible locations of the target vehicles, which reduces the number of candidates being compared, decreasing the time consumption and computational cost. Our vehicle detection model is designed with a color-faster R-CNN, whose inputs are both the texture and color of the vehicles. Meanwhile, velocity estimation is achieved by the Kalman filter, which is a classical method for tracking. Finally, a multimodal data fusion method is applied to integrate these outcomes so that vehicle-tracking tasks can be achieved. Experimental results suggest the efficiency of our methods, which can track vehicles using a series of surveillance cameras in urban areas.

Implantable medical devices are being increasingly used to treat or monitor different medical conditions. For such purposes, wireless is the most desired communication scheme to be implemented in these devices. On the other hand, the wireless scheme increases security threats on these electronic devices, and any possibility of attack on the medical device may have lethal consequences. The patients usually have their implantable medical devices configured and monitored by their doctors. But for practical purposes, most of the time they possess a remote control for daily non-critical operations. This remote control can be considered as an open gate for attackers to target those medical devices and cause major harm. Motivated by this, we analyze in this paper the communication scheme implemented in the wireless devices, having as a starting point an Implantable Insulin Pump to develop a new protocol that can be used in the remote control-implantable device communication, and that will rely on plain text messages to avoid encryption implementation. Finally, we will analyze how the novelties introduced with this protocol can secure such a wireless link.

Wi-Fi has been widely used in our work, home, and many other places, such as hotels and airports. However, the data may be leaked if the access through Wi-Fi is not well-guarded. Wi-Fi hotspots are deployed in an unprecedented speed to facilitate people's lives. The open access nature makes them vulnerable to an evil twin access point (AP), which has the same service set id (SSID) as the legitimate AP and larger signal strength. Current Wi-Fi capable devices are not able to detect the evil twin attack, and will automatically switch to the bogus AP. In this paper, we devise a novel detection scheme based on the commonly used network diagnostic tool traceroute. A remote detection server is set up so that the client-to-server and server-to-client traceroute results are compared. If the evil twin AP is present, it will attempt to conceal the legitimate AP. The inconsistency among the two traceroute results will reveal the evil twin attack. We first present the attack model, then describe the detection scheme in detail. In our implementation, a Nexus 4 smartphone serves as the client, a desktop PC with a USB wireless adapter is set up as the evil twin AP, and the detection service is running on an Amazon EC2 Server. The experimental result demonstrates that our scheme can effectively detect an evil twin attack.

The emergence of big data bringsnewissues and challenges for the data imbalance problem.Therefore, unbalanced data sampling technology has been a hot research topic in the field of big data.However, the existing sampling methods cannot accurately define the harmful and useless samplescontained in the originaldataset. That is, based on the single information of the dataset, a large number of actuallyharmful samples are being used for sampling, which results in a sharp decline in the identifiable performance of the sampled data. In order to overcome the problems caused by only using one kind of information, an unbalanced data hybrid-sampling algorithm based on multi-information fusion(MIFS)is presented in this paper. The MIFS combines the feature information learned by the boostingmodel with the position information of the data to define the sample, and then divides the samples into different subsets by the information contained. According to the definition of samples, the algorithm performs corresponding under-sampling and over-sampling on these subsets. Experiments show that the MIFS method can improve the performance of sampling operations and produce a high F-score and AUC against bothminority and majority classes in the classification of balanced data.

As a popular application, mobile crowd sensing systems aim at providing more convenient service via the swarm intelligence. With the popularity of sensor-embedded smart phones and intelligent wearable devices, mobile crowd sensing is becoming an efficient way to obtain various types of sensing data from individuals, which will make people's life more convenient. However, mobile crowd sensing systems today are facing a critical challenge, namely the privacy leakage of the sensitive information and valuable data, which can raise grave concerns among the participants. To address this issue, we propose an enhanced secure certificateless privacy-preserving verifiable data authentication scheme for mobile crowd sensing, named EPDA. The proposed scheme provides unconditional anonymous data authentication service for mobile crowd sensing, by deploying an improved certificateless ring signature as the cryptogram essential, in which the big sensing data should be signed by one of legitimate members in a specific group and could be verified without exposing the actual identity of the participant. The formal security proof demonstrates that EPDA is secure against existential forgery under adaptive chosen message and identity attacks in random oracle model. Finally, extensive simulations are conducted. The results show that the proposed EPDA efficiently decreases computational cost and time consumption in the sensing data authentication process.

Human behavior data is the basis of behavior analysis, and usually we need to collect large quantities of data before analysis. Most existing data collection methods are labor intensive works in which the volunteers need to be asked to behave naturally under the monitoring of researchers. Identity identification can be used in passive data collection of human behavior analysis systems in big data. Previous researches show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other like fingerprint and iris. As a result, researchers start to explore the ability of WiFi in human identification. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract entities' gait features from both time and frequency domain. Based on these features, Wii realizes identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated in a typical indoor scenario. The results indicate that Wii achieves high identification accuracy with low computational cost and has the potential to work in human behavior analysis systems.

Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.

The demand for multipath transmission is prominent in 5G networks with the deployment of multiple hierarchical access technologies. However, multipath schemes are still not widely adopted due to many reasons, such as deployment challenges and performance reduction under the circumstances of path heterogeneity. Thus, TCP is still in the dominant position of the transport layer protocol for now and for the foreseeable future. Link asymmetry, such as different latency and different bandwidth of different links, is considered to be the main reasons leading to packet reordering, and further result in TCP performance reduction. However, to the best of knowledge, no one has yet given a theoretical model to analyze the relationship between link asymmetry and TCP multipath performance. In this paper, we present a performance analysis model for TCP over multiple heterogeneous networks, which reveals the effect of link asymmetry on TCP throughput. Both bandwidth and delay asymmetry are taken into consideration in the proposed model. The evaluated throughput using the proposed model can accurately fit the simulation results.

Cloud-based malware detection improves the detection performance for mobile devices that offload their malware detection tasks to security servers with much larger malware database and powerful computational resources. In this paper, we investigate the competition of the radio transmission bandwidths and the data sharing of the security server in the dynamic malware detection game, in which each mobile device chooses its offloading rate of the application traces to the security server. As the Q-learning technique has a slow learning rate in the game with high dimension, we have designed a mobile malware detection based on hotbooting-Q techniques, which initiates the quality values based on the malware detection experience. We propose an offloading strategy based on deep Q-network technique with a deep convolutional neural network to further improve the detection speed, the detection accuracy, and the utility. Preliminary simulation results verify the detection gain of the scheme compared with the Q- learning based strategy.

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications is usually sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with fault tolerance in smart grid, which ensures that control center gets the integrated data without revealing user's privacy. Meanwhile, we also consider fault tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.

In this paper, we propose a scheme to protect the Software Defined Network(SDN) controller from Distributed Denial-of-Service(DDoS) attacks. We first predict the amount of new requests for each openflow switch periodically based on Taylor series, and the requests will then be directed to the security gateway if the prediction value is beyond the threshold. The requests that caused the dramatic decrease of entropy will be filtered out and rules will be made in security gateway by our algorithm; the rules of these requests will be sent to the controller. The controller will send the rules to each switch to make them direct the flows matching with the rules to the honey pot. The simulation shows the averages of both false positive and false negative are less than 2%.

With the increasing number of mobile applications and the popularity of cloud computing, the combination of these two techniques that named mobile cloud computing (MCC) attracts great attention in recent years. A promising public key encryption scheme, Attribute-Based Encryption (ABE), especially the Ciphertext Policy Attribute-Based Encryption (CP-ABE), has been used for realizing fine-grained access control on encrypted data stored in MCC. However, the computational overhead of encryption and decryption grow with the complexity of the access policy. Thus, maintaining data security as well as efficiency of data processing in MCC are important and challenging issues. In this paper, we propose an efficient encryption method based on CP-ABE, which can lower the overhead on data owners. To further reduce the decryption overhead on data receivers, we additionally propose a verifiable outsourced decryption scheme. By security analysis and performance evaluation, the proposed scheme is proved to be secure as well as efficient.

Along with the miniaturization of various types of sensors, a mass of intelligent terminals are gaining stronger sensing capability, which raises a deeper perception and better prospect of Internet of Things (IoT). With big sensing data, IoT provides lots of convenient services for the monitoring and management of smart cities and people's daily lives. However, there are still many security challenges influencing the further development of IoT, one of which is how to quickly verify the big data obtained from IoT terminals. Aggregate signature is an efficient approach to perform big data authentication. It can effectively reduce the computation and communication overheads. In this paper, utilizing these features, we construct a verifiable data aggregation scheme for Internet of Things, named VDAS, based on an improved certificateless aggregate signature algorithm. In VDAS, the length of the aggregated authentication message is independent of the number of IoT terminals. Then, we prove that VDAS is existentially unforgeable under adaptive chosen message attacks assuming that the computational Diffie-Hellman problem is hard. Additionally, the proposed VDAS achieves a better trade-off on the computation overheads between the resource-constrained IoT terminals and the data center.

With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band.

Recently, an increasing number of inter-app attacks such as confused deputy attacks, data leakage attacks and collusion attacks spring up. However, there is no perfect defense method against them. As we all know, developers play an important role in android security, but their weak consciousness about the security may lead to inter-app attacks. Therefore, considered for developers, it is important to investigate and try to defend against such attacks in android. This paper presents typical inter-app attacks in android and proposes AutoPatchDroid, an automatic framework to find the vulnerable code in apps and patch them automatically. We firstly find the vulnerable paths from sources to sinks, sources to execution exit points, execution entry points to sinks and execution entry points to execution exit points in the application using static analysis. Then we locate the vulnerable code pieces and insert the patch code to guard against such attacks. AutoPatchDroid prevent inter-app attacks in the application level rather than modifying the kernel or framework. We use DroidBench and IccRE to evaluate our framework, and find that AutoPatchDroid could effectively secure the apps. The runtime overhead introduced by AutoPatchDroid is 1.105% on average.

Trust plays an important role in helping social network user make appropriate decision from abundant social information about products. A variety of social review information (e.g. ratings, voting and tags) emerge with the huge number of products on the Web. How they are utilized for searching and finding appropriate item is investigated. In this paper we propose a user credibility calculation method, which calculates user's credibility through his previous review information. By evaluating facets of every review, an integrated numerical value which denotes the reviewer's credibility can be calculated. This value can be used to rank products, further to help user making appropriate decision. Experiments on social book search database show that this calculation method improves effectively accuracy of recommended items.

The wide spread of mobile devices has also caused the explosive growth of malwares. Application behavior analysis is a popular technique to fight against malwares. However current app behavior analysis methods still have some limitations. For example, many popular dynamic analysis methods are built on Dalvik virtual machines. They cannot disclose the behavior of native code. VMI based methods can overcome this limitation but they're executed in simulated environments. Now malwares can detect where they are running so as to hide the illegal behaviors by anti-forensic techniques. Considering these, we present the DroidRevealer. It is based on kernel-level system calls monitoring and it's running on real android devices. By intercepting and interpreting certain file/network related and android-specific system calls, it can reconstruct app behaviors in real-time. It's difficult to evade as it runs in the kernel. And its results do not simply focus on a single kind of behavior or a single app. Instead it is data oriented, i.e. it monitors how the target data source is used. The result is presented as an intelligible graph which can provide both a good basis for detection and crucial evidence for forensics. Experiments have proved that the performance of our method is acceptable.

The rapid growth of mobile network traffic has posed a serious challenge to the limited spectrum. The United States Federal Communications Commission (FCC) allowed the utilization of unused TV White Space (TVWS) by unlicensed secondary users (SUs). Particularly, the IEEE 802.19.1 standard is proposed to regulate the coexistence of dissimilar or independently operated SU networks and devices on the TV band. In this paper, we propose a fair spectrum allocation scheme for co-existing SU networks under the IEEE 802.19.1 system architecture. The entire heterogeneous wireless system is divided into two levels, and the spectrum allocation is formulated into a four-stage problem. Unlike previous allocation schemes that maximize the aggregated throughput, the aim of our scheme is to maximize the end user satisfactions within each SU network while maintaining fairness among and within the SU networks. Extensive simulations demonstrate the effectiveness of our spectrum allocation scheme.

Web Security Gateway (WSG) is a new type of network security product that maintains the security of trusted networks. In this paper, a WSG model for evaluating WSG performance is presented. This paper advances discussion of previous studies on series services under multiple service windows. The proposed model consists of a two-stage queuing system. The first stage is a network layer simulation. The second stage is thus similar to a parallel hyper-Erlang distribution model. The results of a simulation test verified the feasibility and performance of the proposed model.

Due to the widespread research on Smart Identifier NETwork (SINET), its security has received much attention recently. But most of those attempts consider SINET security from the specific attack perspective. To the best of our knowledge, none so far has paid attention to the security analysis and modeling of separation mechanisms in SINET. Therefore, this paper provides a different approach to security analysis based on Petri net. Our objective is to analyze the separation mechanisms security via the combination of model and state. This method represents the network structure and state transferring by way of Petri net. In addition, it introduces the security analysis method of tokens to explore the potential threatens. Finally, we analyze SINET via the combination of the number, logic, and time series of tokens in Petri net, and present the results. Our results are very promising in using such models to achieve such security objectives.

Network Function Virtualization (NFV) has the potential to significantly reduce the capital and operating expenses, shorten product release cycle, and improve service agility. In this paper, we focus on minimizing the total number of Virtual Network Function (VNF) instances to provide a specific service (possibly at different locations) to all the flows in a network. Certain network security and analytics applications may allow fractional processing of a flow at different nodes (corresponding to datacenters), giving an opportunity for greater optimization of resources. Through a reduction from the set cover problem, we show that this problem is NP-hard and cannot even be approximated within a factor of (1 - o(1))lnm (where m is the number of flows) unless P=NP. Then, we design two simple greedy algorithms and prove that they achieve an approximation ratio of (1 - o(1))ln m + 2, which is asymptotically optimal. For special cases where each node hosts multiple VNF instances (which is typically true in practice), we also show that our greedy algorithms have a constant approximation ratio. Further, for tree topologies we develop an optimal greedy algorithm by exploiting the inherent topological structure. Finally, we conduct extensive numerical experiments to evaluate the performance of our proposed algorithms in various scenarios.

Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.

With the support of cloud computing, large quantities of data collected from various WSN applications can be managed efficiently. However, maintaining data security and efficiency of data processing in cloud- WSN (C-WSN) are important and challenging issues. In this paper, we present an efficient data outsourcing scheme based on CP-ABE, which can not only guarantee secure data access, but also reduce overall data processing time. In our proposed scheme, a large file is divided into several data blocks by data owner (DO) firstly. Then, the data blocks are encrypted and transferred to the cloud server in parallel. For data receiver (DR), data decryption and data transmission is also processed in parallel. In addition, data integrity can be checked by DR without any master key components. The security analysis shows that the proposed scheme can meet the security requirement of C-WSN. By performance evaluation, it shows that our scheme can dramatically improve data processing efficiency compared to the traditional CP-ABE method.

In cognitive radio networks, cooperative spectrum sensing(SS) between cognitive users can improve the detection performance, reduce the testing time. However, a large amount of data interaction restrict its application and increase the consumption of resources. For the demand of cooperative SS, this paper improved the compression algorithm based energy detection which in the early stage of the work, the spectrum data is divided into segments of different characteristic and respectively compressed. The presented algorithm further improved the compression ratio compared to the original algorithm. Verified by the experiment of satellite signals, the experimental results show that the compression performance of this algorithm will be increased several times compared to JPEG, JPEG2000 and detection-based compression algorithm.

Implantable Medical Devices (IMDs) are a growing industry regarding personal health care and monitoring. In addition, they provide patients with efficient treatments. In general, these devices use wireless communication technologies that may require synchronization with the medical team. Even though wireless technology offers satisfaction to the patient's daily life, it is still prone to security threats. Many malicious attacks on these devices can directly affect the patient's health in a lethal way. Using insecure wireless channels for these devices offers adversaries easy ways to steal the patient's private data and hijack these systems. This can cause damage to patients and render their devices unusable. In the aim of protecting these devices, we explore in this paper a new way to create symmetric encryption keys to encrypt the wireless communication held by the IMDs. This key generation will rely on chaotic systems to obtain synchronized Pseudo-Random keys that will be generated separately in the system. This generation is in a way that the communication channel will avoid a wireless key exchange, protecting the patient from key theft. Moreover, we will explore the performance of this generator from a cryptographic point of view, ensuring that these keys are safe to use for communication encryption.

Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.

Implantable medical devices (IMDs) are electronic devices implanted within human body for diagnostic, monitoring, and therapeutic purposes. It is imperative to guarantee that IMDs are completely secured since the patient's life is closely bound to the robustness and effectiveness of IMDs. Intuitively, we have to ensure that only the authorized medical personnel and IMD programmer can access the IMD. However, in recent years, several attacks have been reported which can successfully compromise a number of IMD products, e.g., stealing the sensitive health data and issuing fake commands. Up to now, there is no commonly agreed and well-recognized security standards and the protection of IMD is still an open problem. In this paper, we present a comprehensive survey of the existing literature on IMD security, with a focus on the access control schemes to prevent unauthorized access. Specifically, we first reviewed the security incidents, IMD threat model and the development of regulations for IMD security. Next, we classified existing IMD access control schemes based on architecture, type of keys used, access control channel, and logic. We also analyzed how different access control models can be adopted to secure IMD. Besides, we particularly discussed the viability of online authentication and low/zero power authentication in the IMD context.

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks.

In the smart grid with renewable energy resources (RERs), the residential units (RUs) with distributed energy resources are considered to be both power consumers and suppliers. Specifically, RUs with excessive renewable generations can trade with the utility in deficit of power supplies for mutual benefits. It causes two challenging issues. First, the trading data of RUs are quite sensitive, which should be only accessed by authorized users with fine-grained policies. Second, the behaviors of the RUs to generate trading data are spontaneous and unpredictable, and then the problem is how to guarantee system efficiency and delay tolerance simultaneously. In this paper, we propose a delay-tolerant flexible data access control scheme based on key policy attribute-based encryption for smart grid with RERs. We adopt the secret-sharing scheme to realize a flexible access control with encryption delay tolerance. Furthermore, there is no central trusted server to perform the encryption/decryption. We reduce the computation cost on RUs and operators via a semitrusted model. The analysis shows that the proposed scheme can meet the data security requirement of the smart grid with RERs, and it also has less cost compared with other popular models.

As accurate malware detection on mobile devices requires fast process of a large number of application traces, cloud-based malware detection can utilize the data sharing and powerful computational resources of security servers to improve the detection performance. In this paper, we investigate the cloud-based malware detection game, in which mobile devices offload their application traces to security servers via base stations or access points in dynamic networks. We derive the Nash equilibrium (NE) of the static malware detection game and present the existence condition of the NE, showing how mobile devices share their application traces at the security server to improve the detection accuracy, and compete for the limited radio bandwidth, the computational and communication resources of the server. We design a malware detection scheme with Q-learning for a mobile device to derive the optimal offloading rate without knowing the trace generation and the radio bandwidth model of other mobile devices. The detection performance is further improved with the Dyna architecture, in which a mobile device learns from the hypothetical experience to increase its convergence rate. We also design a post-decision state learning-based scheme that utilizes the known radio channel model to accelerate the reinforcement learning process in the malware detection. Simulation results show that the proposed schemes improve the detection accuracy, reduce the detection delay, and increase the utility of a mobile device in the dynamic malware detection game, compared with the benchmark strategy.

Intrusion detection plays a critical role in security of people's possessions. Approaches such as video-based, infrared-based, RFID, UWB, etc. can provide satisfying detection accuracy. However, they all require specialized hardware deployment and strict using conditions which hinder their wide deployment. Beyond communication, WLANs can also act as generalized sensor networks and there are several researches working on motion detection via WLAN due to its advantages in deployment flexibility, coverage, and cost efficiency. Nevertheless, they are unsuitable for intrusion detection as none of them can accurately detect human motion when the moving speed is very slow. This paper proposes SIED as an accurate method for Speed Independent device-free Entity Detection which is suitable for intrusion detection even when the entity's moving speed is very slow. The influence becomes much smaller when the entity is moving with a very slow speed. Previous methods have the limitations in that their performance downgrades sharply when the entity's moving speed is very slow. Recently, it has been shown that Channel State Information (CSI) at PHY layer of wireless network has the potential to detect moving entities more accurately. In this paper we leverage CSI of 802.11n wireless network and probability technique to detect entities of different moving speeds. SIED captures the variance of variances of amplitudes of each CSI subcarrier, and combines Hidden Markov Model (HMM) to make entity detection a probability problem. We implement SIED using commercial WiFi devices and evaluate our method using two typical testbeds and show that SIED can achieve an average detection accuracy of greater than 98% under different entity moving speed.

This paper proposes a full-duplex physical security model with self-interference remaining. This model doesn't need the assistance of external jamming nodes and it ensures that the uplink and downlink transmission of the full-duplex system can achieve the required secrecy rate. Meanwhile, it creates a base station transmission power optimization problem with flexible constraints and a two-level method to achieve the optimization, so that power optimization can take place in the model with self-interference remaining.

Location-based social networks (LBSNs) make it possible for servers to record users' location histories, mine their life patterns, and infer individual preferences. As an important component of LBSNs, recommender systems gained popularity in recent years. Recommender systems can automatically list candidate locations for users according to their preferences, which is different from traditional search methods. However, making effective recommendations suffers from data sparsity. In order to relieve this problem and achieve high effectiveness, we take context information into consideration and present a personalized location recommender system considering both user preference and local features in this paper. To be specific, we apply Labeled-LDA in user preference learning and local features inference processes, which are denoted as UL-LDA model and CL-LDA model, respectively. Because of this, we can make recommendations even on the condition that users are in a new city and have little information about the city. We evaluate our approach with extensive experiments on a large-scale Foursquare dataset. The experimental results clearly validate the effectiveness of our approach.

A Distributed Denial of Service (DDoS) attack against controllers is one of the key security threats of Software-Defined Networking (SDN). The breakdown of a controller may disrupt a whole SDN network. Nowadays, a novel DDoS means is that the attackers may generate vast new low-traffic flows to trigger malicious flooding requests to overload the controllers. It is difficult to prevent this attack, as the attackers may connect to any interface of any switch in an SDN network. In this paper, we propose an effective detection method, which is designed to detect the DDoS attack and to further locate the compromised interfaces the malicious attackers have connected. We first classify the flow events associated with an interface, then make a decision using Sequential Probability Ratio Test (SPRT), which has bounded false negative and false positive error rates. In addition, we evaluate the performance of the proposed method using DARPA Intrusion Detection Data Sets. We also discuss and compare our method to three other detection methods, which are based on the percentage, count, and entropy of the flows, respectively, and demonstrate the superiority of our method in terms of promptness, versatility and accuracy.

Protecting critical files in file systems is very important to computer systems. To protect critical files, the VMI-based Real-time File-system Monitor tools are promising options. However, these tools are always operation-based and introduce high overhead. The operation-based approaches intercept some kind of file operation to monitor critical files. The selected file operation is intercepted by the monitor whenever it is being executed. As file operation are high-frequency, the operation-based methods always result in the high performance degradation. In this paper, we present a VMI-based low overhead real-time critical file monitor method, CFWatcher, to meet the performance requirements of real-time monitor tools. CFWatcher is a target-based monitor tool which means it only intercepts the file operations accessing the user-defined critical files, and then obtains enough information to check the rules. The overhead of CFWatcher is related to the frequency of the target being accessed. Besides monitoring critical files, CFWatcher can take actions to prevent the illegal access if there is any rule violation. We implemented the prototype of CFWatcher and then evaluated the performance. Experimental results show that the overhead of our approach is low.

Location-based service (LBS) is useful for many applications. However, LBS has raised serious concerns about users' location privacy. Utilizing the computation and storage capacity of smart phones, we propose a novel system architecture, called Location Privacy Protection for Smartphone (LPPS), to provide a privacy-preserving top-k query. LPPS does not rely on a trust third party (TTP), nor does it requires LBS servers to change their business model. The main idea of LPPS is to rank the Points of Interests (POIs) on the client side of the application using a small amount of metadata and then to make a request to the LBS server for real-time and detailed information about the POIs. Based on LPPS, we propose a novel metric called location indistinguishability to evaluate the privacy level of users in the proposed scheme. Then, we propose two dummy-POI selection algorithms to generate a superset of the actual top-k POIs when the query cannot meet the privacy requirement. Our experimental results demonstrate the validity and practicality of the proposed schemes.

Most applications in urban Vehicular Ad hoc NETworks (VANETs) rely on information sharing, such as real-time traffic information queries, advertisements, etc. However, existing data dissemination techniques cannot guarantee satisfactory performance when a lot of information requests come from all around the network. Because these pieces of information are useful for multiple users located in various positions, it is beneficial to spread the cached copies around. Existing work proposed caching mechanisms and conducted simulations for validation, but there is a lack of theoretical analysis on the explicit caching effects. In this paper, we present the cache coverage ratio as the metric to quantify the caching effects, and theoretical analysis is given based on reasonable assumptions for urban VANETs, through which we find the affecting factors include vehicle density, transmission range, ratio of caching vehicles, etc. We deduce the quantitative relationship among them, which have similar forms as the cumulative density function of an exponential distribution. We conduct intensive simulations, which verify the theoretical analysis results match quite well with the simulated reality under different scenarios.

Recently, with the support of mobile cloud computing, large number of health-related data collected from various body sensor networks can be managed efficiently. However, it is an important and challenging issue to keep data security and data privacy in cloud-integrated body sensor network (C-BSN). In this paper, we present a novel secure access control mechanism MC-ABE (Mask Certificate-Attribute Based Encryption) for cloud-integrated body sensor networks. A specific signature is designed to mask the plaintext, then the masked data can be securely outsourced to cloud severs. An authorization certificate composing of the signature and related privilege items is constructed that is used to grant privileges to data receivers. To ensure security, a unique value is chosen to mask the certificate for each data receiver. The analysis shows that the proposed scheme has less computation cost and storage cost compared with other popular models.

Device-to-device (D2D) communication, which is defined as a direct communication between two mobile users without traversing the Base Station (BS) or the core network to offload the increasing traffic to the user equipments, is one of the key technologies in the fifth generation (5G) of wireless communication systems. Discovery and communication are the basic two features to fulfill the need for the D2D communication. However, Most of existing D2D studies focused on the communication issues always assume that the discovery is completed. In this paper, we propose two strategies of device discovery and access scheme for the 5G cellular networks. Then the performance analysis based on two dimensional discrete time Markov process model is provided. In addition, we present numerical simulation on the Matlab platform. The simulation results demonstrate the viability of the proposed scheme.

Abstract Histograms are important tools for data mining and analysis. Several differentially private publishing schemes for histograms have been proposed recently. Existing differentially private histogram publication schemes have shown that histogram reconstruction is a promising idea for the improvement of publication histograms' accuracy. However, none of these have properly considered the problem outliers in the original histogram, which can cause significant reconstruction errors. Based on the problem, the publication of histogram outliers under differential privacy, this paper puts forward a publication method for histograms with outliers under differential privacy: Outlier‐HistoPub. Our method deals with the count sequence of the original histogram first, using a “global sort” to reduce the degree of alternative distribution (a concept proposed in this paper), which may eliminate the influence of outliers during reconstruction. To avoid individual privacy leakage in the reconstruction process, an exponential mechanism is used to select the most similar adjacent bins of the uniformity distribution histogram to merge each time, and the Laplace mechanism is utilized to generate noisy data to perturb the count sequence of the reconstruction histogram. Experiments prove that the method proposed in this paper can improve the efficiency and accuracy of histogram publication. Copyright © 2016 John Wiley & Sons, Ltd.

Given the varying communication environment between the media cloud and users, there is a need to ensure the most significant part of a video will be successfully transmitted. Although there exist some techniques to evaluate the significance of video data in traditional video coding methods, such as H.264, the evaluation algorithms are often simple and inaccurate. This paper presents a novel significance evaluation method for video data based on compressed sensing. Specifically, we propose a method to obtain a trained dictionary directly by using the measurements of the video data, and then keep the sparse components and generate a saliency map. Since the sparse components can reflect the essential parts of videos, we discuss how to analyze the area and distribution of salient regions. At last, we present a computing method that gives the degree of significance of a frame. Experimental results show that the proposed saliency map reflects the focus points of humans. The method can be used in the distribution of video data over “wireless” transmissions and provide good video quality to mobile users.

Abstract Most applications in urban vehicular ad hoc networks (VANETs) rely on information sharing, such as real‐time traffic information queries, and advertisements. However, existing data dissemination techniques cannot guarantee satisfactory performance when amounts of information requests come from all around the network. Because these pieces of information are useful for multiple users located in various positions, it is beneficial to spread the cached copies around. Existing work proposed caching mechanisms and conducted simulations for validation, but there is a lack of theoretical analysis on the explicit caching effects. Because of the complex urban environment and high mobility of vehicles, quantifying the caching effects on the VANET performance is quite challenging. We present the cache coverage ratio as the metric to measure the caching effects, and theoretical analysis is given based on reasonable assumptions for urban VANETs, through which we find the affecting factors include vehicle density, transmission range, and ratio of caching vehicles. We deduce the quantitative relationship among them, which have similar forms as the cumulative density function of an exponential distribution. We also consider the impact of vehicle mobility to predict the future cache effect on surrounding roads of the caching area. We conduct intensive simulations, which verify that the theoretical analysis results match quite well with the simulated reality under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract A large number of new data‐consuming applications are emerging, and many of them involve mobile users. In the next generation of wireless communication systems, device‐to‐device (D2D) communication is introduced as a new paradigm to offload the increasing traffic to the user equipment. Before the traffic transmission, D2D discovery and access procedure is the first important step which needs to be completed. In this paper, our goal is to design a device discovery and access scheme for the fifth generation cellular networks. We first present two types of device discovery and access procedures. Then we provide performance analysis based on the Markov process model. In addition, we present numerical simulation on the Vienna Matlab platform. The simulation results demonstrate the viability of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract The Internet of vehicles (IoV) is an emerging networking technology, which can support information sharing and interactions among users, vehicles, and infrastructures. Various applications can be provided by IoVs, and they have very different quality‐of‐service (QoS) requirements. It is a great challenge to design an efficient MAC protocol to meet the different QoS demands of various applications in IoVs, because of unreliable links and high vehicle mobility. On the other hand, cooperative communication is effective in mitigating wireless channel impairments by utilizing the broadcast nature of wireless channels. In this paper, a multi‐channel cooperative clustering‐based MAC (MCC‐MAC) protocol, under the Dedicated Short Range Communication (DSRC) multi‐channel architecture, is presented to improve the transmission reliability of safety messages and provision QoS for different applications in IoVs. Further, we analyze the performance of MCC‐MAC, in terms of average transmission delay. In addition, extensive simulations with ns‐2 are conducted to demonstrate the performance of the proposed MCC‐MAC. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract With the advances of sensing, wireless communication, and mobile computing, mobile crowdsourcing has become a new paradigm for data collection and retrieval that has attracted considerable attention. This paper addresses the fundamental research issue in mobile crowdsourcing: Which participants should be selected as winners in each time slot with the aim of maximizing the total utility of the service provider in the long term? First, a double‐sided combinatorial auction model is introduced to describe the relationships between the mobile users and requesters from the perspective of supply and demand at a given time. Then, the coupling between the utility values of the system in different time slots is investigated. Based on the aforementioned analyses, this paper proposes a context‐aware participant recruitment mechanism, in which the mobile crowdsourcing system dynamically adjusts the participant recruitment mechanism depending on the ratio between the numbers of mobile users and requesters. Context‐aware participant recruitment consists of two main components: (1) a heuristic algorithm based on the greedy strategy to determine the winning participants and (2) a critical payment scheme, which guarantees the rationality of the proposed mechanism. Finally, extensive simulations demonstrate that the proposed mechanism achieves high system utility in the long term. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.

One of the most logical applications of face recognition for authentication is on mobile handset devices. However, face recognition still faces challenges in providing environment tolerance: being able to compensate for changes in light conditions within an environment where authentication is occurring, due to users carrying their mobile handset devices to different locations with varying and unpredictable sources of illumination. Existing face recognition systems operate by finding fiduciary points relative to the area of the entire face, which becomes their weakness when they are not used in applications where light conditions are fixed and controlled. This research investigates Local Binary Patterns (LBP), an image encoding technique whose origins lie in texture analysis, in order to overcome the problems faced by existing face recognition systems and provide tolerance to variable light conditions. This research aims to utilize LBP on modern mobile handset device hardware that is "off-the-shelf": utilizing only the most basic and widely available onboard imaging hardware and processing capability provided on mobile handset devices of the present day. We have performed rigorous experimentation with LBP both on large databases of images of human faces, as well as developing mobile handset software that was deployed to real users and tested in a field environment. Our experimentation indicates that LBP is capable of being used to develop face recognition systems that provide environment tolerance, potentially finding practical use as a component of mobile device authentication applications.

Nowadays Hadoop is popular among businesses and individuals for its low costs, convenience, and fast speed. However, this also makes it the goal of data leakage attacks as sensitive data stored with an HDFS infrastructure grows rapidly. Therefore, it is important to investigate such attacks in Hadoop. Several works have been done on improving the security of Hadoop, but hardly any have been done on data leakage investigation. This paper presents a typical data leakage attack scene in Hadoop and proposes Haddle (Hadoop Data Leakage Explorer), a forensic framework composed of automatic analytical methods and on-demand data collection based on two stages. With the assistance of Haddle, investigators can find the stolen data, find the perpetrator who stole the data, and reconstruct the crime scene. Also, Haddle can help improve the audit mechanism of Hadoop.

This paper presents Griffin, a scheme of geometric routing on flat names to conduct massive content distribution and retrieval. A tree-based metric space T is proposed according to the concept of hierarchical division of symbol space. In Griffin, the network topology is embedded into the T-space, and content names are mapped to the T-space. Content publication and retrieval are supported by geometric routing in the T-space. Different from previous embedding schemes, Griffin constructs the T-space according to the network topology before embedding. In contrast to prior name resolution schemes, Griffin operates directly on the network topology without establishing an overlay. The correctness of Griffin is proved by the greediness of geometric routing. The experiments by simulation demonstrate that Griffin is efficient and scalable.

Current memory forensic methods mainly focus on evidence collection and data recovery. A little work is about how to automatically identify malwares from many unknown processes and analyze their behaviors in high semantic level so as to collect related evidences. In fact, in real cases, investigators are often faced with large number of processes that they have no knowledge of. Although current malware detection tools could provide some help, they usually can't illustrate the purposes, abilities and behavior details of malwares and are thus often not fit for the forensic requirements. In this paper, we present a framework named Detective to cope with these issues. Given a set of unknown processes, Detective can classify benign and malware processes automatically. This is implemented by HNB classifying algorithm and a Dynamic-Link Libraries-based model. Detective could then explain malware behaviors in high semantic level through clustering and frequent item sets mining techniques. Besides, Detective sheds light on evidence collection by the information obtained from previous steps. Detective is applicable for both online and offline forensic scenarios. Experiments on real-world malware set have proved that the accuracy of Detective is above 90% and the time cost is only several seconds.

Digital right protection in P2P systems is attracting more and more attentions. In this paper, we present a new stochastic-encryption-based Digital Rights Management (DRM) scheme for P2P content delivery networks. The files are encrypted such that unpaid users cannot access the plaintext content. We exploit the random characteristics of P2P to increase the key space, which can defense collusion attacks. We add piece validation policy during a download process to prevent poisoning attacks. In our scheme, peers make a payment after downloading, and this prevents user loss due to download failures (caused by the dynamics of P2P). Our scheme does not have frequent user authentications or state maintenance. Analysis and simulation experiments show that our scheme can defend against collusion attacks and poisoning attacks with a fairly high probability.

Despite the increasing capabilities, mobile devices still cannot satisfy the computation requirement of many applications. Intuitively, this can be solved by outsourcing tasks to external resources such as a remote server, cloud, or closely deployed cloudlet. However, all of them require extra infrastructures. In this paper, we consider a proximate-mobile-device based communication system in which all tasks and resources are under the control of a central scheduler. We propose a friendship-based task scheduling algorithm to address the contentions when resources are not sufficient. We also present two attack models including the denial-of-service (DoS) attack and the collusion attack. We evaluate the performance of the proposed algorithm along with another contribution-based task scheduling algorithm through extensive experiments.

Location-based social network (LBSN) has grown exponentially over the past several years. Given its high utility value, LBSN, however, has raised serious concerns about users' location privacy. Although users may avoid releasing geo-content in sensitive locations, this, however, does not necessarily prevent the adversary from inferring users' privacy through spatial-temporal correlations and historical information. In this paper, we introduce a new location privacy problem: context-aware location privacy protection (CLPP) problem where the privacy requirements of users are not constant and isolated. We propose a novel metric to quantify the privacy risks. Then the CLPP is formalized as how to accurately and efficiently evaluate whether the users' published geo-content meet the user's privacy requirement. To achieve online evaluating, we design two novel algorithms to calculate the correlation between the locations. Eventually, our experimental results demonstrate the validity and practicality of the proposed strategy.

Abstract Memory forensics is an important technique for protecting network security and fighting against computer crimes. It has developed greatly in the past decade, because memory can provide more reliable information that other evidence sources do not contain. However, nowadays, when investigating network criminal cases, the Gigabyte (GB) and even Terabyte (TB) level memory and many such dumps have made memory analysis a difficult task. And investigators usually have to deal with complex operating system (OS) data structures, which they have little knowledge of. So how to analyze memory evidence automatically so as to find the hidden criminal behavior and reconstruct the scenario in an understandable way has become an important problem. This paper presents an automatic memory analysis methodology based on data correlation. Through analyzing key OS data structures and utilizing a clustering algorithm, this methodology can discover the relationships among processes, files, users, Dynamic‐link library (DLLs), and network connections. By describing these relationships as correlation graphs, our methods can reorganize these independent memory evidences and disclose their meanings in a high semantic level. Experiments have proved that these correlation graphs can help investigators find hidden criminal behavior and reconstruct the criminal scenarios. And as we know, now, little work is in this field. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In this paper, we propose a novel scheme that can achieve data privacy by hybrid cloud, which consists of public and private cloud, and reduce storage and computation in private cloud, as well as communication overhead between private and public cloud. Meanwhile, we propose a novel algorithm to process private image data. Our experimental results show that (1) our algorithm achieves data privacy but only takes about 1/1000, time of the Advanced Encryption Standard algorithm and (2) the delay of our hybrid cloud approach (including the private and public cloud communications) is only 3%–5% more compared with the traditional public cloud‐only approach. Copyright © 2015 John Wiley & Sons, Ltd.

Wireless insulin pumps have been widely deployed in hospitals and home healthcare systems. Most of them have limited security mechanisms embedded to protect them from malicious attacks. In this paper, two attacks against insulin pump systems via wireless links are investigated: a single acute overdose with a significant amount of medication and a chronic overdose with a small amount of extra medication over a long time period. They can be launched unobtrusively and may jeopardize patients' lives. It is very urgent to protect patients from these attacks. We propose a novel personalized patient infusion pattern based access control scheme (PIPAC) for wireless insulin pumps. This scheme employs supervised learning approaches to learn normal patient infusion patterns in terms of the dosage amount, rate, and time of infusion, which are automatically recorded in insulin pump logs. The generated regression models are used to dynamically configure a safe infusion range for abnormal infusion identification. This model includes two sub models for bolus (one type of insulin) abnormal dosage detection and basal abnormal rate detection. The proposed algorithms are evaluated with real insulin pump. The evaluation results demonstrate that our scheme is able to detect the two attacks with a very high success rate.

Nowadays mobile phones have been widely used, and Android is one of the most popular mobile operating system. The security issue of Android has caught great concerns among mobile users and researchers. In this paper, we study the vulnerabilities related of phone cameras. Specifically, we discover and present several camera-based attacks including the basic camera attack and advanced passcode inference attacks. We implement these attacks on real phones (with anti-virus software installed) and demonstrate the feasibility and effectiveness of the attacks. Furthermore, a lightweight defense scheme is proposed to secure phones against these attacks.

As one of the most widely-used Peer-to-Peer applications, BitTorrent system has gained a lot of successes in last decade. BitTorrent has been extensively studied in the literature. However, few research studies vulnerabilities of BitTorrent, and the abuses of BitTorrent have already impeded the wide adoption of the system. In this paper, we study a common attack on BitTorrent - the Leechers attack, which tries to take over valid connections of legitimate users without paying for them. It is harmful to a swarm if certain percentage of users perform the selfish behavior. We present an analytical model for the Leechers attack and we use the model to estimate the distribution of valid connections. Our model discovers the important factors that determine how harmful the attack is, and it can quantitatively predict the duration of downloading process under the attack. The real-world experimental results match well with our model, which demonstrates that the model is correct and useful.

Position information of nodes in vehicular ad hoc networks (VANETs) plays a key role in geographic routing. A sender or intermediate node employs position information of its neighbors and destination node to make routing decision. Under a greedy forwarding algorithm, the neighboring node closest to the destination node is selected as the next hop. Hence, it is critical in geographic routing to ensure that the selected next hop has a better position than other neighboring nodes. Position information is usually propagated to local nodes through periodical beaconing. In most geographic routing protocols, each node broadcasts beacons in a fixed interval, but this method can not always achieve both position accuracy and low overhead. In this paper, we propose a contention-based adaptive position update (CAPU) scheme for intermittently connected VANETs. CAPU concentrates on the position accuracy of the next hop when data transmission happens. If the position deviation of the next hop is greater than the permitted deviation range, the next hop updates its position. A special next hop timeout approach is proposed to find and delete the unreachable next hop as soon as possible. CAPU can find key nodes in local topology for greedy forwarding and intermittent connectivity. In addition, contention beacons broadcasted by key nodes maintain the local topology. Experimental results show that the proposed approach provides key position information for routing decision and exhibits better routing performance with acceptable overhead.

In this paper, we present a novel scheme for auditing Service Level Agreement (SLA) in a semi-trusted or untrusted cloud. A SLA is a contract formed between a cloud service provider (CSP)and a user which specifies, in measurable terms, what resources a the CSP will provide the user. CSP's being profit based companies have incentive to cheat on the SLA. By providing a user with less resources than specified in the SLA the CSP can support more users on the same hardware and increase their profits. As the monitoring and verification of the SLA is typically performed on the cloud system itself it is straightforward for the CSP to lie on reports and hide their intentional breach of the SLA. To prevent such cheating we introduce a framework which makes use of a third party auditor (TPA). In this paper we are interested in CPU cheating only. To detect CPU cheating, we develop an algorithm which makes use of a commonly used CPU intensive calculation, transpose matrix multiplication, to randomly detect cheating by a CSP. Using real experiments we show that our algorithm can detect CPU cheating quite effectively even if the extent of the cheating is fairly small.

With the increase use of Android mobile phones, more Android malwares are being developed. Android malware detection becomes a crucial task. In this paper, we present a permission-combination-based scheme for Android malware detection. The Android malware detection scheme is based on permission combinations declared in the application manifest file. We obtain the permission combinations that are requested frequently by malwares but rarely by benign applications. We generate rule sets based on the permission combinations. Our experimental results show that the malware detection rate is up to 96%, and the benign application recognition rate is up to 88%. Our experimental results with real malwares show that the Android malware detection scheme is very efficient and effective.

The growing popularity of Android-based smart-phones have led to the rise of Android based malware. In particular, profit-motivated malware is becoming increasingly popular in Android malware distribution. These malware typically profit by sending premium-rate SMS messages and/or make premium-rate phone calls from infected devices without user consent. In this paper, we investigate the telephony framework of the Android operating system and propose a novel process user-identification (UID) based online detection scheme. Our scheme can effectively detect premium-rate and background SMS messages as well as premium-rate phone calls initiated by malware. We implemented our detection system on a Samsung Google Nexus 4 running Android Jelly Bean and tested the effectiveness of detecting real malware from Android markets. The experimental results show that our scheme is efficient and effective in detecting background messages and premium-rate messages and phone calls. Our scheme can detect and block all the background and premium-rate SMS messages and phone calls initiated by popular malware.

Recent years have witnessed the increasing threat of phishing attacks on mobile platforms. In fact, mobile phishing is more dangerous due to the limitations of mobile phones and mobile user habits. Existing schemes designed for phishing attacks on computers/laptops cannot effectively address phishing attacks on mobile devices. This paper presents MobiFish, a novel automated lightweight anti-phishing scheme for mobile platforms. MobiFish verifies the validity of web pages and applications (Apps) by comparing the actual identity to the identity claimed by the web pages and Apps. MobiFish has been implemented on the Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as fake Facebook Apps. The result shows that MobiFish is very effective in detecting phishing attacks on mobile phones.

With the rapid development of mobile computing, more and more mobile devices, such as smart phones and tablets are able to access Internet. As these mobile devices are usually battery powered, energy efficiency is a very important issue. For most mobile applications, energy saving should be considered at the design stage. Of course, security application is no different. Public key cryptography plays an important role in network security, and it is still essential in mobile computing despite it needs high energy consumption. Considering Elliptic Curve Cryptography (ECC) is easy to perform in hardware and needs lower energy than other public key algorithms. We propose an ECC-based certificate-less public key cryptography scheme. The scheme is lightweight and can save energy for mobile devices. Firstly, it does not need certificate to prove the authenticity of a public key, which can save energy for certificate transmission. Secondly, it is constructed on the traditional ECC instead of bilinear pairing, which makes it lightweight and can save energy for computation. In addition, it avoids the key escrow issue, which makes it has higher security strength than traditional public key cryptography. These advantages make it very suitable for resources-constrained mobile devices.

Security of wireless medical devices is critical for patient safety because security attacks may directly hurt patients' health. In this paper, we design a novel access control scheme based on bi-channel and multi-factor authentication for wireless medical devices. Our scheme utilizes near field communication (NFC) to perform device pairing, which supports key exchange between a device and a reader in short communication range (<= 6cm) with bounded response time. To further defend against attacks when a malicious reader is placed within the device's communication range in crowded situations, we design a crowd detection algorithm using WiFi and user's smart phone to assist the key exchange. Our analyses and experiments show that our security schemes are effective and efficient.

For the purposes of information security and privacy between readers and tags, identity authentication is a significant issue for radio frequency identification (RFID) systems. In this study, the authors propose a novel security group‐based key array authentication protocol, which is suitable for a large scale RFID environment. Based on a key array, this protocol can generate an authentication key for each pair of reader and tag with lower storage. Adding an identifier update phase, they design the authentication process passing the formal analysis from GNY. The security and performance analysis results show that the protocol they present can achieve better security than previous protocols in resisting external and internal attacks, with lower storage and acceptable communication and computation load.

ABSTRACT In this paper, we study the important issue of verifying service‐level agreement (SLA) with an untrusted cloud and present an SLA verification framework that utilizes a third‐party auditor (TPA). A cloud provides users with elastic computing and storage resources in a pay‐as‐you‐go way. An SLA between the cloud and a user is a contract that specifies the computing resources and performances that the cloud should provide to the user. A cloud service provider (CSP) has incentives to cheat on the SLA, for example, providing a user with less central processing unit and memory resources than specified in the SLA, which allows the CSP to support more users and make more profits. A malicious CSP can easily disrupt the existing SLA monitoring/verification techniques by interfering with the monitoring/measurement process. A TPA resolves the trust dilemma between a CSP and its users. Under the TPA framework and the untrusted‐cloud threat model, we design two effective testing algorithms that can detect an SLA violation of the virtual machine memory size. Using real experiments, we demonstrate that our algorithms can detect cloud cheating on a virtual machine's memory size (i.e., SLA violations). Furthermore, we show that our testing algorithms can defend various attacks from a malicious CSP, which tries to hide an SLA violation. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT A base station is the controller and the data‐receiving center of a wireless sensor network. Hence, a reliable and secure base station is critical to the network. Once an attacker locates the base station, he or she can do many damages to the network. In this paper, we examine the base station location privacy problem from both the attack and defense sides. First, we present a new attack on base station: parent‐based attack scheme (PAS). PAS can locate a base station within one radio (wireless transmission) range of sensors in high‐density sensor networks. Different from existing methods, PAS determines the base station location on the basis of parent–child relationship of sensor nodes. Existing base station protection schemes cannot defend against PAS. Second, on the basis of PAS, we propose a two‐phase parent‐based attack scheme (TP‐PAS). Our simulation results demonstrate that TP‐PAS is able to determine the base station successfully in both low‐density and high‐density sensor networks. Then, to defend against PAS and TP‐PAS, we design a child‐based routing protocol and a parent‐free routing protocol for sensor networks. Our theory analysis and experiment results show that the parent‐free routing protocol has more communication cost and less end‐to‐end latency compared with the child‐based routing protocol. Copyright © 2012 John Wiley & Sons, Ltd.

Many studies use cryptographic technologies to protect sensitive data in public cloud. However, these approaches may introduce large overheads. Recently, hybrid cloud started to gain a lot of attentions. A hybrid cloud consists of a private cloud and a public cloud. Hybrid cloud allows users to store sensitive data in their private cloud and hence enables efficient and secure data outsourcing. In this paper, we consider a new hybrid cloud model “Cloud-in-Cloud” (CIC). Our CIC model uses a new architecture to form a hybrid cloud: placing a small number of private computers (i.e., a small private cloud) within a public cloud. The private cloud can be used to store sensitive user data. Furthermore, it is within the public cloud, so the communications between private and public clouds have small overhead. And then we study how to protect a private cloud that locates within a semi-trusted environment. We present two methods that can detect attacks that try to obtain data and information in the private cloud. Our methods are able to efficiently detect physical attacks, such as the cold boot attack and the USB autorun attack. Experimental results show that our methods have small overhead.

Cloud computing allows individuals and organizations outsource their data to cloud server due to the flexibility and cost savings. However, data privacy is a major concern that hampers the wide adoption of cloud services. Data encryption ensures data content confidentiality and fine-grained data access control prevents unauthorized user from accessing data. An unauthorized user may still be able to infer privacy information from encrypted data by using indexing techniques. In this paper, we investigate the problem of sensitive information leakage caused by orthogonal use of these two kinds of techniques. Based on that, we propose “core attribute”-aware techniques that can ensure privacy of outsourced data. The techniques focus on confidential attribute set of outsourced data. We adopt k-anonymity technique for the attribute indexes to prevent user from inferring privacy from unauthorized data. We formally prove the privacy-preserving guarantee of the proposed mechanism. Our extensive experiments demonstrate the practicality of the proposed mechanism, which has low computation and communication overhead.

Android developers cache data to improve the performance of their applications. Caching is the technique of transparently storing data such that future requests can be accessed more quickly. At times when a mobile device is not under heavy use the cached data, including sensitive data, can remain on the device for an extensive period of time. This poses a security risk, especially when developers do not take the necessary security measures to protect their users' sensitive information. While there does exist ways to clear application caches built within the Android operating system and third-party applications, these approaches require the user to manually perform these tasks. This paper presents a dynamic cache cleaner that more aggressively pushes out unused cache data. We also present other possible solutions to more effectively manage the cache.

Android Honeycomb operating system is widely used for tablet devices, such as Samsung Galaxy Tab. The Android system programs are usually efficient and secure in memory management. However, there has been a few security issues reported that show Android's insufficient protection to the kernel. In this work, we reveal a new security pitfall in memory management that can cause severe errors and even system failures. Existing security software for android do not detect this pitfall, due to the private implementation of Android kernel. We then discuss two vulnerabilities introduced by this pitfall: 1) malicious programs can escalate the root-level privilege of a process, through which it can disable the security software, implant malicious codes and install rootkits in the kernel; 2) deny of service attacks can be launched. Experiments have been conducted to verify these two vulnerabilities on Samsung Galaxy Tab 10.1 with Tegra 2 CPU. To protect systems from these vulnerabilities, we proposed a patching solution, which has been adopted by Google.

The growing concerns about the privacy of data stored in public cloud have hindered the widespread adoption of cloud. On one hand, large part of data, such as medical data, has a lot of images, and this kind of data may be private. On the other hand, the cloud service providers have the full access of data, and they may bleach the data for financial or other reasons. The traditional method to protect the privacy of data is to employ cryptographic algorithms, which unavoidably introduces heavy computation. Another way is hybrid cloud consisting of public and private cloud. The sensitive data is separated from non-sensitive data, and only the non-sensitive data is outsourced to public cloud. If we use hybrid cloud method directly, all the private images have to be stored in private cloud, which makes the adoption of cloud computing meaningless. Besides achieving data privacy, we should reduce computation and storage overhead in private cloud, as well as communication overhead between private and public cloud. In this paper, we propose a novel scheme to achieve the above goals. We test our scheme in real network environments (including Amazon EC2). We also propose a novel algorithm to process private image data. Our experimental results show that: (1) Our algorithm achieves data privacy but only takes about 1/1,000 the time of the AES algorithm. (2) The delay of our hybrid cloud approach (including the private and public cloud communications) is only 3%-5% more compared to the traditional public-cloud-only approach.

As cloud computing services are gaining popularity, many organizations are considering migrating their large-scale computing applications to cloud. Different cloud service providers (CSPs) may have different computing platforms and billing methods. Most cloud customers don't know which CSP is more suitable for their applications and how much computing resource should be purchased. To address this issue, in this paper, we present a performance prediction scheme that allows a cloud customer to accurately predict computing resource (e.g., running time) for an application. The proposed scheme identifies application's control flow and scaling blocks, constructs a miniature version program to run in local machines, and then replays it in cloud to get the performance ratio between local and cloud. Our real-network experiments show that the scheme can achieve high prediction accuracy with low overhead.

Wireless insulin pumps have been widely deployed in hospitals and home healthcare systems. Most of these insulin pump systems have limited security mechanisms embedded to protect them from malicious attacks. In this paper, two attacks against insulin pump systems via wireless links are investigated: a single acute overdose with a significant amount of medication, and chronic overdose with an insignificant amount of extra medication over a long time period, e.g., several months. These attacks can be launched unobtrusively and may jeopardize patients' lives. It is very important and urgent to protect patients from these attacks. To address this issue, we propose a novel patient infusion pattern based access control scheme (PIPAC) for wireless insulin pumps. This scheme employs a supervised learning approach to learn normal patient infusions pattern with the dosage amount, rate, and time of infusion, which are automatically recorded in insulin pump logs. The generated regression models are used to dynamically configure a safety infusion range for abnormal infusion identification. The proposed algorithm is evaluated with real insulin pump logs used by several patients for up to 6 months. The evaluation results demonstrate that our scheme can reliably detect the single overdose attack with a success rate up to 98% and defend against the chronic overdose attack with a very high success rate.

In this paper, we study several important issues that can be used to prevent pirated content propagation in BitTorrent (BT) Distributed Hash-Tables (DHT) networks. We design a system called PPBD to stop pirated content propagation by utilizing several attacking methods. First, the system can efficiently deal with massive concurrent connections to reduce bandwidth consumption, schedule peers to cooperate and optimize the protection methods according to clients. Second, we construct two mathematical models for BT DHT attacks, and we theoretically analyze the system performance. Third, we take into account some countermeasures of different BT clients and make corresponding optimizations of our PPBD system. Our realworld experiments show that: (1) our system can extend the download duration at least three times by the fake-block attacking method and it is more effective in a small swarm; (2) DHT index poison and routing pollution methods can limit the sharing swarm to a small swarm.

With the advent of cloud computing, data owner is motivated to outsource their data to the cloud platform for great flexibility and economic savings. However, the development is hampered by data privacy concerns: Data owner may have privacy data and the data cannot be outsourced to cloud directly. Previous solutions mainly use encryption. However, encryption causes a lot of inconveniences and large overheads for other data operations, such as search and query. To address the challenge, we adopt hybrid cloud. In this paper, we present a suit of novel techniques for efficient privacy-aware data retrieval. The basic idea is to split data, keeping sensitive data in trusted private cloud while moving insensitive data to public cloud. However, privacy-aware data retrieval on hybrid cloud is not supported by current frameworks. Data owners have to split data manually. Our system, called Prometheus, adopts the popular MapReduce framework, and uses data partition strategy independent to specific applications. Prometheus can automatically separate sensitive information from public data. We formally prove the privacy-preserving feature of Prometheus. We also show that our scheme can defend against the malicious cloud model, in addition to the semi-honest cloud model. We implement Prometheus on Hadoop and evaluate its performance using real data set on a large-scale cloud test-bed. Our extensive experiments demonstrate the validity and practicality of the proposed scheme.

In recent years, several random key pre-distribution schemes have been proposed for wireless sensor networks. However, the problem of key and node revocation has received fewer attentions. In this paper, we present a novel random key revocation protocol, which is suitable for large scale networks and removes compromised information efficiently. The proposed revocation protocol can guarantee network security and has less memory consumption and communication overhead. With the combination of centralized and distributed revocations, the protocol achieves both timeliness and accuracy for revocation. The simulation results show that our protocol has better performance than existing protocols in terms of increasing revocation validity and revocation velocity, and prolonging the network lifetime.

With the widespread use of multi-core processors, task scheduling for multi-core processors has become a hot issue. Many researches have been done on task scheduling from various perspectives. However, the existing task scheduling algorithms still have some drawbacks, such as low processor utilization rate, high complexity, and so on. This paper presents a task scheduling algorithm for multi-core processors, which is based on priority queue and task duplication. In the proposed algorithm, the Directed A cyclic Graph (DAG) is used to build a task model. Based on the model, task critical degree, task reminder, task execution time and the average communication time are all considered as the priority metrics. A priority based task dispatching list is set up by comprehensive analysis and calculating the priority for each task. Then interval insertion and task duplication strategies are employed to map tasks to processors, which can decrease the communication cost, improve the processor utilization rate and shorten the schedule length. Our experiments show that the proposed algorithm has better performance and lower complexity than the existing scheduling algorithms.

Cloud computing services offer elastic computing and storage to end-users over the Internet in a pay-as-you-go way. Many businesses have started using cloud computing. A Service Level Agreement (SLA) between a cloud service provider (CSP) and a user is a contract that specifies the resources and performances that the cloud should provide. However, a CSP has the incentive to cheat on SLA, e.g., providing users with less CPU and memory resources than that specified in the SLA, which allows the CSP to support more users and make more profits. Unfortunately, there are no tools to allow users to verify the SLA. We study the important issue of verifying SLA in a semi-trusted (or untrusted) cloud. In this paper, we focus on the verification of CPU speed, which is an important metric in cloud SLA. We propose a lightweight stealthy test algorithm that can check if a CSP provides the CPU speed as specified in the SLA. Using real experiments, we show that the algorithm can detect cloud cheating on CPU speed (i.e., SLA violations) in a stealthy way.

We consider the distributed channel allocation problem in an asymmetrical opportunistic spectrum access (OSA) system where each secondary user possibly has different channel reward even in the same channel due to geographic dispersion. We formulate this problem as a Gale-Shapley stable theorem using game theory to optimize the sum reward of all secondary users. It is challenging to achieve the stable matching of user-channel pairs without centralized control and prior knowledge of channel availability statistics. In this paper, we present a novel decentralized order-optimal learning Gale-Shapley scheme (OLGS) in which secondary users learn from their local history data and individually adjust their behaviors in a time-varying OSA system. The proposed scheme eliminates collisions among secondary users by a one-to-one user-channel matching policy. It also achieves stable spectral allocations using learning method without assuming known channel parameters and independent of information exchange among secondary users. Simulation results show that the system regret of the OLGS solution grows with time at the logarithmic order with low complexity.

In this paper, we propose a distributed precoder design algorithm for suppressing the inter-cell interference(ICI) and maximizing the average throughput of a multi-cell multi-user multi-input multi-output(MU-MIMO) system. Motivated by the recent results of the distributed signal-to-leakage-plus-noise ratio(SLNR) model for ICI coordination(ICIC), we design a joint optimization algorithm to achieve the pareto-optimal average system throughput. Existed SLNR-based algorithms only consider downlink optimization problem of one optimized factor, which will bring a restriction to the further improvement of the system performance. In our proposed algorithm, the SLNR-based downlink precoder and the transmitting power factor will be designed from a joint optimization problem, and the closed-form pareto-optimal solutions of both optimized factors can be obtained through matrix analysis and decomposition techniques. Simulation results show that the proposed distributed joint precoder design algorithm can significantly increase the average cell throughput and improve the resource efficiency while effectively reducing the system overhead.

BitTorrent (BT) is a widely-used peer-to-peer (P2P) application. Most of BT's characteristics (except the topology) have been studied extensively by measurement approaches. In this paper, we deploy a measurement system to examine some performance-related topology properties of BT. Our goal is to provide a measurement view of the real-world BT topologies and to verify the previous estimations via simulations and real-world experiments. We observe that at the steady stage, a BT topology has short distances and low clustering coefficients, and its degree-frequency exhibits a Gaussian-like distribution. These indicate that a BT network is very close to a random network rather than a scale-free network or a small world. The proportion of peers with large download percentages is very high at the steady stage, showing that the swarm is robust from the resource perspective. We also find out that most high-degree peers have a very fast download speed. However, the low Spearman's rank correlation coefficient indicates that there is no strong correlation between the peer connection degree and the download speed. Different from previous results, we find that the diameter of a BT network at the initial stage is small even when 95% of peers use the peer exchange extension.

Peer-to-Peer (P2P) networks have been widely used in various Internet applications. However, P2P networks also cause serious concerns of copyrighted contents piracy: P2P helps to pirate copyrighted contents, which impedes wide application of P2P networks. In this paper, we investigate several important issues on the ways to prevent pirated content propagation in P2P networks. We propose a system to stop pirated content propagation by utilizing several attacks to BitTorrent (BT). First, we design a system that can handle a large number of concurrent connections to reduce bandwidth consumption. Second, we build two mathematical models for BT attacks, including leechers behavior model and fake-block behavior model, and we analyze the performance of the system using these models. Third, we optimize BT clients by taking into account different implementations and counter-measure designs. We conducted several experiments in real networks, the results of which verified that our system is suitable for most current BT clients, and BT clients' download duration is extended at least three times longer, which is much better than the results reported in the literature.

Due to the unattended operation nature, nodes in Unattended Wireless Sensor Networks (UWSNs) are susceptible to physical attacks. Once a sensor is compromised, the adversary will be able to learn all its secrets. While some previous works tried to address the node self-healing issue in UWSNs, little effort has been devoted to ensure the sustainability of node self-healing. In this paper, we present a novel sustainable node self-healing protocol for UWSNs. We generate unpredictable random data for key update and thus the node self-healing capability doesn't decrease when the number of attack rounds increases. We show both analytically and through simulation experiments that our protocol provides efficient and sustainable node self-healing capabilities with small overheads.

In this paper, we present a novel secure auditing scheme for cloud computing systems. Several auditing schemes have been proposed for the cloud, which periodically trigger the auditing function. These schemes are designed to monitor the performance and behavior of the cloud. One major problem with these kind of schemes is that they are vulnerable to the transient attack (also known as the timed scrubbing attack). Our secure auditing scheme is able to prevent the transient attack via modification of the Linux auditing daemon - auditd, which creates attestable logs. Our scheme utilizes the System Management Mode (SMM) for integrity checks and the Trusted Platform Module (TPM) chip for attestable security. Specifically, we modify the auditing daemon protocol such that it records a hash of each audit log entry to the TPM's Platform Configuration Register (PCR), which gives us an attestable history of every command executed on the cloud server. We perform real experiments on two cloud servers and the results show that the overhead of our scheme is very small.

In this paper, we present an efficient routing protocol for Mobile Hybrid Wireless Networks (MHWNs), which consists of an infrastructure wireless network and a few mobile ad hoc networks. MHWNs have several advantages over traditional wireless networks (such as cellular networks and WLANs). For example, MHWNs can achieve higher throughput, larger coverage, and better load balancing cross cells. In this paper, we present an efficient routing protocol for MHWNs. The protocol utilizes node location information, moving speed, and remaining battery life for routing decision. The protocol is energy efficient (for mobile nodes) and can find robust routing path. We evaluate its performance by NS-2 simulations. Our results show that the protocol significantly reduces the routing overhead, and the routing path is relatively stable.

BitTorrent (BT) is one of the most popular Peer-to-Peer (P2P) network applications. Most characteristics (except the topology) of BT network have been examined extensively by measurement approaches. In this work, we deploy a measurement system to study the performance-related properties of BT topologies. We also use our measurement system to verify some previous simulation and experiment results obtained by other researchers. Different from previous results, we observe that a BT swarm has short distance, low clustering coefficient and Gaussian-like degree-frequency distribution. This indicates that a BT swarm is very close to a random network rather than a scale-free network or a small world. We observe that the diameter of a BT network at the initial stage is small even when 95% of peers use the peer exchange extension but the networks are not fully connected at the steady stages.

In this paper we study the important issue of verifying Service Level Agreement (SLA) in a semi-trusted (or untrusted) cloud. Cloud computing services promise elastic computing and storage resources in a pay-as-you-go way. A SLA between a cloud service provider (CSP) and a user is a contract which specifies the resources and performances that the cloud should offer. However, the CSP has the incentive to cheat on SLA, e.g., providing users with less CPU and memory resources than that specified in the SLA, which allows the CSP to support more users and make more profits. A malicious CSP can disrupt the existing SLA monitoring/verification techniques by interfering the monitoring/measurement process. Therefore, we present a SLA verification framework that leverages a third party auditor (TPA). Under the TPA framework, we propose an effective testing algorithm that can detect SLA violations of physical memory size in virtual machine (VM). Using real experiments, we show that the algorithm can detect cloud cheating on VM memory size (i.e., SLA violations). Furthermore, our algorithm can defend various attacks from a malicious CSP, which tries to hide a SLA violation.

A base station (BS) is the controller and the data receiving center of a wireless sensor network. Hence, a reliable and secure BS is critical to the network. Once an attacker locates the BS, he can do a lot of damages to the network. In this paper, we study the BS location protection issue. First, we present a new attack on BS: the Parent-based Attack Scheme (PAS). The PAS can locate a BS within one radio (wireless transmission) range of sensors. Different from existing methods, the PAS determines the BS location based on parent-child relationship of sensor nodes. The PAS cannot be defended by existing BS protection schemes. To defend against the PAS, we design a new parent-free (PF) secure routing protocol for sensor networks. Our simulation results show that the PF protocol has small communication and computation costs, while ensuring the security of the BS.

The secret sharing scheme was invented by Adi Shamir and George Blakley independently in 1979. In a (k, n)-threshold linear secret sharing scheme, any k-out-of-n participants could recover the shared secret, and any less than k participants could not recover the secret. Shamir's secret sharing scheme is more popular than Blakley's even though the former is more complex than the latter. The reason is that Blakley's scheme lacks determined, general and suitable matrices. In this paper, we present two matrices that can be used for Blakley's secret sharing system. Compared with the Vandermonde matrix used by Shamir's scheme, the elements in these matrices increase slowly. Furthermore, we formulate the optimal matrix problem and find the lower bound of the minimal maximized element for k=2 and upper bound of the minimal maximized element of matrix for given k.

In Peer-to-Peer (P2P) networks, security is a challenging issue due to decentralization. In this paper, we propose an effective distributed login framework for P2P networks. User profile availability is a critical issue in P2P networks. Within the distributed login framework, we propose a new Reed-Solomon erasure code scheme leveraging the Pascal matrix that can guarantee user profile availability. Our performance and security analyses show that: (1) the distributed login framework provides high availability and low redundancy rate; and (2) the new erasure code has low computation and memory overheads.

BitTorrent (BT) is a well-known Peer-to-Peer (P2P) downloading protocol and has been implemented in several versions. New features and extensions used to improve performance of BitTorrent systems also bring some security issues. In this paper, we analyze potential DDoS vulnerabilities of BT and its Peer Exchange extension. We show the ways of launching connection-exhausted DDoS attacks. Our experiments demonstrate these attacks are persistent and incur few costs for the attacker. By analyzing the main causes we find that both the defect of implement and the lack of trust and authentication mechanism are to blame, while the latter is critical. To defend against the DDoS attacks, we propose a score-based peer Reputation Exchange (REX) mechanism. Using REX, the score of a malicious peer is less than that of a good peer after several iterations, hence has less chance to be connected. REX makes it difficult to launch a DDoS attack and it can effectively mitigate the effect of the attack.

ABSTRACT The p‐cycle design problem has been extensively studied because it can provide both ring‐like fast self‐protection speed and spare capacity efficiency of path protection scheme. However, p‐cycle provisioning for dynamic traffic has not been fully addressed. Most related works have not considered link capacity in the construction of p‐cycles, which may cause problems in practice because the protection paths may not have enough backup bandwidth. In this paper, with the consideration of link capacity, we present a sufficient and necessary condition that guarantees p‐cycles for providing enough protection bandwidth. Based on this condition, we propose an effective solution to provide connections for dynamic requests with the property that each link used for a connection is protected by a p‐cycle. Simulation results show that our dynamic p‐cycle provisioning solution outperforms the traditional path protection scheme. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Anonymous communication is very important for many wireless sensor networks, because it can be used to hide the identity of important nodes, such as the base station and a source node. In sensor networks, anonymous communication includes several important aspects, such as source anonymity, communication relationship anonymity, and base station anonymity. Existing sensor network anonymous schemes either cannot achieve all the anonymities or have large computation, storage, and communication overheads. In this paper, we propose an efficient anonymous communication protocol for sensor networks that can achieve all the anonymities while having small overheads on computation, storage, and communication. We compare our anonymous communication protocol with several existing schemes, and the results show that our protocol provides strong anonymity protection and has low overheads. Copyright © 2011 John Wiley & Sons, Ltd.

Anonymous communication is a challenging task in resource constrained wireless sensor networks (WSN). However, anonymity is important for many sensor networks, in which we want to conceal the location and identify of important nodes (such as source nodes and base stations) from attackers. Existing WSN anonymous protocols either cannot achieve complete anonymity, or have large computation and/or storage overheads. In this paper, we present an efficient anonymous communication protocol for sensor networks. Our protocol can achieve sender/source anonymity, communication -relationship anonymity, and the base station anonymity simultaneously, while having small overheads on computation, storage and communication.

In many applications of Wireless Sensor Networks (WSN), the source of an event needs to be protected. In resource constrained WSN, providing source anonymity is a challenging task. A traditional approach for hiding source in WSN is to let all nodes generate dummy data packets even if they have no event to report. However, this kind of approach introduces large overhead. In order to reduce the large overhead of sending dummy data packets, we propose using a much shorter control packet to achieve source anonymity. The short control packets are used to coordinate the transmissions of dummy data packets, which prevent revealing the source node and hence provides source anonymity in WSN. We evaluate the performance of our anonymity scheme via ns-2 simulations. The simulations show that our scheme has much less traffic overhead than an existing anonymity scheme.

Wireless mesh network (WMN) is a rapid deployed, self organized and multi-hop wireless network. The wireless and distributed natures of WMNs make them subject to various kinds of attacks, which raise a great challenge in securing these networks. Most existing security mechanisms are based on cryptographic keys where a high degree key management services are in demand. In this paper, we present an effective key management scheme which seeks an encryption key assignment such that the induced network is connected and well protected against potential eavesdropping attacks. Compared with previous work, our scheme assigns the available encryption keys among all the nodes in the network. The simulation results show that our scheme out performs previous schemes through providing a network that is resistant against malicious eavesdropping attack.

Implantable Medical Devices (IMDs) are widely used to treat chronic diseases. Nowadays, many IMDs can wirelessly communicate with an outside programmer (reader). However, the wireless access also introduces security concerns. An attacker may get an IMD reader and gain access to a patient's IMD. IMD security is an important issue since attacks on IMDs may directly harm the patient. A number of research groups have studied IMD security issues when the patient is in nonemergency situations. However, these security schemes usually require the patient's participation, and they may not work during emergencies (e.g., when the patient is in comma) for various reasons. In this paper, we propose a light-weight secure access control scheme for IMDs during emergencies. Our scheme utilizes patient's biometric information to prevent unauthorized access to IMDs. The scheme consists of two levels: level 1 employs some basic biometric information of the patient and it is lightweight; level 2 utilizes patients' iris data for authentication and it is very effective. In this research, we also make contributions in human iris verification: we discover that it is possible to perform iris verification by comparing partial iris data rather than the entire iris data. This significantly reduces the overhead of iris verification, which is critical for resource-limited IMDs. We evaluate the performance of our schemes by using real iris data sets. Our experimental results show that the secure access control scheme is very effective and has small overhead (hence feasible for IMDs). Specifically, the false acceptance rate (FAR) and false rejection rate (FRR) of our secure access control scheme are close to 0.000% with suitable threshold, and the memory and computation overheads are acceptable. Our analysis shows that the secure access control scheme reduces computation overhead by an average of 58%.

In this paper, we study an emerging type of wireless network - Hybrid Wireless Networks (HWNs). A HWN consists of an infrastructure wireless network (e.g., a cellular network) and several ad hoc nodes (such as a Mobile ad hoc network). Forming a HWN is a very cost-effective way to improve wireless coverage and the available bandwidth to users. Specifically, in this work we investigate the issue of bandwidth allocation in multi-hop HWNs. We propose three efficient bandwidth allocation schemes for HWNs: top-down, bottom-up, and auction-based allocation schemes. In order to evaluate the bandwidth allocation schemes, we develop a simulated HWN environment. Our simulation results show that the proposed schemes achieve good performance: the schemes can achieve maximum revenue/utility in many cases, while also providing fairness. We also show that each of the schemes has merit in different application scenarios.

Security is an important issue for sensor networks deployed in hostile environments, such as military battlefields. The low cost requirement precludes the use of tamper resistant hardware on tiny sensor nodes. Hence, sensor nodes deployed in open areas can be compromised and used to carry out various attacks on the network. In this paper, we consider the collision attack that can be easily launched by a compromised (or hostile) node: a compromised node does not follow the medium access control protocol and cause collisions with neighbor transmissions by sending a short noise packet. This attack does not consume much energy of the attacker but can cause a lot of disruptions to the network operation. Due to the wireless broadcast nature, it is not trivial to identify the attacker. In this paper, we propose a distributed scheme that is based on low-cost hardware and can effectively identify the source of a collision attack. Our scheme is based on analyzing physical-layer Received Signal Strength Index (RSSI) readings. We show that correct identification of an adversarial node can be achieved with greater than 85% accuracy. We further present a technique that degrades gracefully as the background noise increases.

Implantable Medical Devices (IMDs) have been widely used to treat chronic diseases such as cardiac arrhythmia and diabetes. Many IMDs are enabled with wireless communication capabilities and can communicate with an outside programmer/reader wirelessly. With the rapid growth of IMDs, IMD security becomes a critical issue since attacks on IMDs may directly harm the patient. Typical IMDs have very limited resource in terms of energy, computation and storage. In this research, we identify a new kind of attacks on IMDs - Resource Depletion (RD) attacks that could deplete IMD resources (e.g., battery power) quickly. The RD attacks could reduce the lifetime of an IMD from several years to a few weeks. The attacks can be easily launched but can not be defended by traditional cryptographic approaches. In this paper, we propose to utilize the patient's IMD access pattern and we design a novel Support Vector Machine (SVM) based scheme to address the RD attacks. Our SVM-based scheme is very effective in defending the RD attacks. Our experimental results show that the average detection rate of the SVM-based scheme is above 90%.

Routing is an essential operation in wireless sensor networks. Most existing routing protocols are designed for homogeneous sensor networks. Recent studies show that a homogeneous sensor network has a poor fundamental performance limit. To achieve better performance, we adopt a Heterogeneous Sensor Network (HSN) model. In this paper, we present an efficient NEar-optiMal rOuting (NEMO) protocol for HSNs. We evaluate the performance of NEMO through extensive simulation experiments. Our results show that NEMO can find near-optimal routes in an HSN and has very small overhead.

Wireless sensor networks consist of a large number of sensors and have been broadly used for intrusion detection in surveillance systems. To guarantee detection quality, such networks are usually over-engineered, i.e., more than required sensors are deployed and remain active in order to cover each point in the monitored field with a high probability at any time instance. Existing sensor scheduling schemes based on the point coverage model tightly weave a sensor "net" that is unnecessarily dense. Intuitively, when the size and the shape of intrusion objects are considered, any net with holes no smaller than the size of the intrusion object would work fine. With this design philosophy in mind, we build a new mathematical model to investigate the impact of size and shape of intrusion objects on network configuration. We derive analytical results that provide practitioners with insights on how to weave an effective sensor "net" for intrusion object detection with minimum number of active sensors.

Establishing pair-wise keys in wireless sensor networks is a challenging task due to the hostile environment and limited resources in sensor nodes. Many key management schemes have been proposed for sensor networks. However, most existing schemes are designed for homogeneous sensor networks where all of the nodes have the same or similar capabilities. Research has shown that better performance can be achieved by forming a heterogeneous sensor network. In this paper, we present an efficient post-deployment key management scheme designed for heterogeneous sensor networks. The scheme does not assume any prior knowledge about sensor deployment and location. Our scheme takes advantage of a few powerful high-end sensor nodes and achieves efficient and effective key establishment in a sensor network. The performance evaluation shows efficiency and effectiveness measures for our scheme.

To achieve better performance, we adopt a heterogeneous sensor network (HSN) model. In many applications, the locations of some sensor nodes are controllable. In this paper, first we propose a novel density-varying deployment scheme for high-end sensors (H-sensors) in an HSN. The scheme solves the bottleneck problem in typical many-to-one sensor networks. We then study the optimal placement of H-sensors whose locations are controllable. The goal is to use the minimum number of H-sensors for ensuring successful data delivery, coverage and connectivity in a network for a given lifetime. We present an effective H-sensor placement scheme that can simultaneously achieve coverage, connectivity and data relay requirements while uses a small number of H-sensors. Both theoretical proofs and simulation results demonstrate that the proposed H-sensor placement scheme achieves very good performance.

Wireless sensor networks have been widely used in environment and habitat monitoring, as well as in military applications such as battlefield surveillance. In this paper, we focus on detecting intruders in such surveillance systems. Our goal is to optimize the network coverage when the network is deployed to detect an intrusion object with the shape of a disc or a rectangle. We study how the size and shape of the intrusion object influence the configuration of the sensor network. We prove many mathematical results related to detection probability and intrusion coverage intensity and study the asymptotic properties of these detection metrics. We also study the problem of maximizing network lifetime under some QoS constraints. We prove the existence of the solution and derive the explicit form of the solution under certain conditions.

The p-cycle design problem has been extensively studied because it can provide both ring-like fast self-protection speed and spare capacity efficiency of path protection scheme. However, p-cycle provisioning for dynamic traffic has not been fully addressed. Most related works have not considered link capacity in the construction of p-cycles, which may cause problems in practice because the protection paths may not have enough backup bandwidth. In this paper, with the consideration of link capacity, we present a sufficient and necessary condition that guarantees p-cycles for providing enough protection bandwidth. Based on this condition, we propose an effective solution to provide connections for dynamic requests with the property that each link used for a connection is protected by a p-cycle. Simulation results show that our dynamic p-cycle provisioning solution outperforms the traditional path protection scheme.

Abstract In this paper, we study the rekeying issue in IEEE 802.16e WiMAX networks. The existing rekeying scheme—the Multicast and Broadcast Rekeying Algorithm (MBRA) unicasts new keys to each subscriber station (SS). This scheme does not scale well since it incurs large communication overheads when the number of SSs increase. In our work, first we propose a general tree‐based rekeying scheme, which is more efficient than the MBRA. We also formulate an optimization problem to determine the optimal tree structure for given number of SSs. Furthermore, we present a novel and efficient rekeying scheme for WiMAX networks. Our new rekeying scheme utilizes efficient security schemes and the WiMAX network application feature. Both analysis and performance evaluation show that our rekeying scheme can significantly reduce the communication overheads. Copyright © 2009 John Wiley & Sons, Ltd.

Previous research on sensor network security mainly considers homogeneous sensor networks, where all sensor nodes have the same capabilities. Research has shown that homogeneous ad hoc networks have poor performance and scalability. The many-to-one traffic pattern dominates in sensor networks, and hence a sensor may only communicate with a small portion of its neighbors. Key management is a fundamental security operation. Most existing key management schemes try to establish shared keys for all pairs of neighbor sensors, no matter whether these nodes communicate with each other or not, and this causes large overhead. In this paper, we adopt a Heterogeneous Sensor Network (HSN) model for better performance and security. We propose a novel routing-driven key management scheme, which only establishes shared keys for neighbor sensors that communicate with each other. We utilize Elliptic Curve Cryptography in the design of an efficient key management scheme for sensor nodes. The performance evaluation and security analysis show that our key management scheme can provide better security with significant reductions on communication overhead, storage space and energy consumption than other key management schemes.

Block ciphers encrypt a fixed size block of plaintext at a time to produce a block of ciphertext. Stream ciphers encrypt stream data, such as voice or Telnet traffic, one bit or more bits at a time. The cipher feedback mode is a stream cipher implemented by a block cipher via multiple stages, and in each stage one bit or a number of bits of plaintext are encrypted at a time. In this paper, we study error performance of the stream-based cipher feedback mode in an unreliable wireless channel in terms of throughput. We model performance of the cipher feedback mode in terms of the probability that part of or the whole ciphertext can not be successfully decrypted, and the throughput by adopting the cipher feedback mode. We explicitly derive the optimal number of stages in the cipher feedback mode to achieve the optimal throughput, given an error rate in a wireless network. We also prove that for the cipher feedback mode, the whole ciphertext is successfully decrypted if and only if the whole ciphertext is successfully transmitted.

We propose a hierarchical pipeline paging (HPP) for multi-tier hierarchical cellular networks, in which different tiers overlay with one another to provide overlapped coverage of cellular service, and each mobile terminal can be paged in any tier of a network. Paging requests (PRs) are queued in different waiting queues, and multiple PRs in each waiting queue are served in a pipeline manner. We study HPP, hierarchical sequential paging (HSP), and hierarchical blanket paging (HBP) schemes analytically in terms of discovery rate, total delay, paging delay, and cost. It is shown that HPP scheme outperforms both HBP and HSP schemes in terms of discovery rate while maintaining the same cost as HSP scheme. The HPP scheme outperforms HSP scheme in terms of total delay and has a lower total delay than HBP scheme when traffic load is high.

Wireless sensor networks are usually over-engineered, i.e., more than required sensors are deployed and remain active in order to cover each point in the monitored field with a high probability at any time instance. Intuitively, when the size and the shape of objects are considered, any net with holes no smaller than the size of the object would work fine. In this paper, we build a mathematical model to investigate the impact of size and shape of objects on network configuration. We derive analytical results that provide practitioners with insights on how to weave an effective sensor "net" for object detection with minimum number of active sensors.

Security is crucial for wireless sensor networks deployed in the military and other hostile environments. Due to the limited transmission range, a sensor node may need multiple hops of transmissions to deliver a packet to the base station. An attacker can launch the selective forwarding attack and drop a portion of packets for which it needs to relay while forward the rest. Selective forwarding attack is hard to detect, since packet drops in sensor networks may be caused by unreliable wireless communications or node failures. In this paper, first we describe an efficient scheme for reporting packet drops, then we present an effective scheme for detecting the selective forwarding attack in a heterogeneous sensor network. The scheme utilizes powerful high-end sensors and is based on the sequential probability ratio test. Our extensive simulations show that the proposed scheme achieves high detection ratio and very low false alarm rate.

An attacker can capture sensor nodes operating in an unattended environment, load malicious code in the nodes, and then redeploy the compromised nodes into the network. These nodes can launch various kinds of insider attacks and cause severe damage to the network. Thus, fast and efficient detection of compromised nodes in wireless sensor networks is a critical security issue. In this paper, we adopt a Heterogeneous Sensor Network (HSN) model to achieve better security and performance. We present an effective and efficient scheme for detecting compromised nodes by utilizing powerful high-end sensors in HSNs. We compare our scheme with an existing detection scheme. The performance evaluation shows that our scheme is more efficient and has much higher detection rate than the existing one.

Security is critical for wireless sensor networks deployed in military, homeland security and other hostile environments. In this paper, we study a security issue related with broadcast in sensor networks. Due to the broadcast nature of wireless communications, often it is more efficient to broadcast packets to sensor nodes. Typically, broadcast authentication is achieved by digital signatures. Since digital signature operations are expensive for small sensor nodes, an attacker can launch a serious denial of service (DoS) attack. That is, an attacker may forge a large number of broadcast messages with digital signatures, and then force sensor nodes to verify these signatures, which can cause them run out of power. In this paper, we present an effective and efficient scheme that can defend such DoS attack on broadcast authentication. Our performance evaluation shows that the scheme is much more secure and efficient than an existing scheme.

Wireless sensor networks are widely used for monitoring applications. The monitored area and the intrusion object are mostly three dimensional. In this paper, we are particularly interested in a sensor network used for monitoring a field to detect intrusion objects such as submarine in some sea area, or goods on shelves in warehouse. In these applications, sensors float in different depth of water or on different height of shelves in three dimensional situations. Since sensor nodes have limited energy supply, sensor networks may be configured to put some sensor nodes in sleep mode to save energy. This is a special case of a randomized scheduling algorithm. In this paper, we study the performance of several randomized scheduling algorithms in terms of intrusion coverage intensity when an intrusion object is considered to occupy a three dimensional space. We study the impact of the size of intrusion object on the sensor networkpsilas configuration.

In this paper, we study MPEG-4 transmissions over the IEEE 802.11e wireless local area networks (WLANs). In (Y. Xiao et al., 2007), we provided a simulation of MPEG-4 using OPNET over WLANs, and simulation results show that a higher throughput does not always mean a better quality of MPEG-4 video. Therefore, in this paper, we propose two schemes to enhance MPEG4 transmissions over WLAN: 1) we propose a prioritized frame cross-layer transmission scheme between the medium access control (MAC) layer and the application layer, and 2) we adopt a measurement admission control scheme for IEEE 802.11e. Simulation results show advantages of the proposed schemes.

Abstract Poison message failure is a mechanism that has been responsible for large‐scale failures in both telecommunications and IP networks. We design a fault management framework that integrates passive diagnosis and active diagnosis to identify the poison message and prevent network instability. Passive diagnosis uses real‐time inference and reasoning techniques to analyze network information and generates a probability distribution of the poison message, and the probability distribution is used in active diagnosis for further failure identification. In active diagnosis, message filtering is used to block suspect message types. Blocking messages affects network performance and service. The tradeoff of message filtering is formulated as a Markov Decision Process (MDP). The large size of the state space makes it impractical to use traditional techniques to solve the MDP. Con sequently, we use a combination of reinforcement learning and feature‐based function approximation to obtain a suboptimal policy. Extensive simulations demonstrate the effectiveness of passive diagnosis, and show that the suboptimal policy performs significantly better than a well‐known heuristic policy. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Sensor nodes are powered by battery and have severe energy constraints. The typical many‐to‐one traffic pattern causes uneven energy consumption among sensor nodes, that is, sensor nodes near the base station or a cluster head have much heavier traffic burden and run out of power much faster than other nodes. The uneven node energy dissipation dramatically reduces sensor network lifetime. In a previous work, we presented the chessboard clustering scheme to increase network lifetime by balancing node energy consumption. To achieve good performance and scalability, we propose to form a heterogeneous sensor network by deploying a few powerful high‐end sensors in addition to a large number of low‐end sensors. In this paper, we design an efficient routing protocol based on the chessboard clustering scheme, and we compute the minimum node density for satisfying a given lifetime constraint. Simulation experiments show that the chessboard clustering‐based routing protocol balances node energy consumption very well and dramatically increases network lifetime, and it performs much better than two other clustering‐based schemes. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Many data‐gathering protocols for wireless sensor networks use clustering technology for prolonging network lifetime. Cluster‐based protocols reduce the total energy consumption via data aggregation and balance energy consumption via clusterhead rotation. However, most existing protocols focus on load balance within each cluster. The energy consumption of the entire network is still unbalanced and this uneven energy dissipation can significantly reduce network lifetime. We propose an even energy dissipation protocol (EEDP) for efficient cluster‐based data gathering in wireless sensor networks. In EEDP, sensor data are forwarded to the base station (BS) via multiple chains of clusterheads. Each chain uses a rotation scheme to balance energy consumption among clusterheads and avoid the formation of a hot spot. We developed efficient algorithms to organize clusterheads into multiple chains, such that the traffic load is evenly distributed among different chains. Analysis and simulation results show that EEDP achieves better load balance than several existing protocols and significantly increases network lifetime. Copyright © 2007 John Wiley & Sons, Ltd.

Most existing research regarding sensor network security considers homogenous sensor networks. To achieve better security and performance, we adopt a heterogeneous sensor network (HSN) model that consists of physically different types of sensor nodes. In this paper, we present an efficient key distribution scheme for HSNs, which takes advantage of powerful high-end sensor nodes. The scheme establishes a secure communication topology in the network in an efficient and secure manner. We show that the scheme has smaller memory requirements than a popular key distribution scheme and it is more resilient against the node compromise attack.

Security is critical for sensor networks used in military, homeland security and other hostile environments. Previous research on sensor network security mainly considers homogeneous sensor networks, i.e., all sensor nodes are the same. Research has shown that homogeneous ad hoc networks have poor performance and scalability. Furthermore, many security schemes designed for homogeneous sensor networks have high communication overhead, computation overhead, and/or large storage requirement. To achieve better security and performance, we adopt a heterogeneous sensor network (HSN) model. In this paper, we present an efficient key management scheme that takes advantage of the powerful high-end sensors in HSN. The performance evaluation and security analysis show that the key management scheme provides better security with low complexity and significant reduction on storage requirement, compared to existing sensor key management schemes.