Deep neural networks are increasingly vulnerable to physically deployable backdoor attacks, which manipulate real-world objects to induce targeted model failures. However, current physical backdoor attacks predominantly rely on perpetually visible triggers appended to target objects. These methods inevitably expose attack traces during the deployment phase, risking human suspicion prior to activation. In this paper, we propose a conditionally-visible physical backdoor attack, which can only be activated under specific optical conditions and thereby overcomes the risk of being detected after deployment and before the attack. Specifically, to ensure robust and reliable activation, we design irregular polygonal pattern as triggers to against across environmental variations. Moreover, we introduce a dual-phase mechanism (dormant and activated) to enable stealthy deployment. Our trigger remains invisible and dormant under non-attack conditions, leaving no physical traces. It activates instantaneously under specific illumination, inducing the target model to perform the desired behavior. We conduct experiments on traffic sign recognition tasks to compare our attack with six digital and seven physical attacks, and assess its performance against potential defenses. Extensive experimental results demonstrate the effectiveness, stealthiness, and robustness of our attack.

Advanced Persistent Threats (APTs) represent sophisticated cyberattacks that evade detection through stealthy, multistage operations, posing severe risks to critical infrastructure and organizational security. Due to their ability to effectively capture contextual information of attack behaviors, provenance graphs have emerged as a promising approach for APT detection. However, traditional binary edges in provenance graphs fail to represent the collaborative nature of APT attacks, where multiple entities coordinate in single operations, and local graph structures cannot capture the long-range dependencies across attack stages. To address these challenges, we propose HyperDetector, a novel hypergraph-based method for APT detection. First, we introduce hypergraph representation for provenance data, where hyperedges naturally connect multiple entities involved in system events, preserving the higher-order relational structures that characterize APT behaviors. Second, we employ block self-attention mechanisms that enable global reasoning across distant hypergraph regions, effectively linking dispersed attack indicators throughout the system. Through the synergistic integration of these approaches, HyperDetector achieves comprehensive understanding of both localized multi-entity collaborative behaviors and system-wide attack propagation patterns. Extensive evaluations across multiple prominent datasets demonstrate that HyperDetector outperforms state-of-the-art methods, showcasing its effectiveness for robust and holistic APT detection. Additionally, we make our code and datasets publicly available to facilitate reproducibility and foster further research in this critical area.

The vast number of devices and the complexity of requirements present significant challenges in ensuring the security of Non-Terrestrial Internet of Things (NT-IoT). Although existing studies have proposed methods like to defend against data theft and network interference attacks, there is still a need for more in-depth research on detecting data-level attacks in NTNs. Moreover, the vast and diverse nature of network traffic presents significant challenges in traffic modeling and feature extraction. Hypergraph neural networks have gained considerable attention because of capabilities in data modeling and feature extraction. However, most existing hypergraph neural networks are tailored for specific applications and are not adaptable to the detection of malicious encrypted traffic. To address these challenges, we firstly propose a hypergraph neural network-based malicious encrypted traffic detection framework to enhance the resilience of NT-IoT, enabling attack detection across unmanned aerial vehicles, base stations and satellites. Then, we introduce a Multidimensional Encrypted Traffic HyperGraph Network (METHGN). METHGN models the encrypted traffic from network, connection and time dimensions using hypergraph and uses hypergraph convolution network to extracts and fuse features. We conducted comparative experiments on IoT and The Onion Router Network encrypted traffic datasets for different classification tasks. Extensive experiments demonstrate the effectiveness and superiority of our approach.

Recent studies show that deep learning-based traffic sign recognition systems are vulnerable to backdoor attacks. These compromised models can be activated to misclassify traffic signs when exposed to specific backdoor patterns during inference. Nevertheless, existing attack methods rely on exogenous triggers (e.g., stickers or patches) that introduce external features to associate backdoor patterns with target labels, significantly increasing attack complexity. In this paper, we propose FlipBAT, a stealthy endogenous backdoor attack method that uses the image’s self-flipping as the built-in trigger, eliminating the need for external trigger patterns. Our attack supports two distinct attack modes: a multi-class backdoor attack that enables flexible target diversification via cyclic mappings, and a single-class backdoor attack that achieves higher stealthiness by minimally perturbing the source class. Extensive experiments conducted on two standard traffic sign recognition datasets (GTSRB and BelgiumTS) across three different victim models demonstrate that FlipBAT effectively establishes robust mappings between backdoor images and target classes. Notably, our method achieves efficient backdoor attacks with significantly lower poisoning rates compared to conventional approaches. Our method has also been shown to be robust against state-of-the-art backdoor defenses.

Uncrewed aerial vehicle (UAV) networks are increasingly exposed to widespread and various network attacks due to their fully distributed nature and the limited defensive capabilities of individual devices. Existing defense strategies rely on network connectivity and UAV status information, which overlook information of network traffic. Malicious traffic detection offers a promising solution to achieve fine-grained attack detection. However, the dynamic nature and complexity of UAV networks limit the effectiveness of traditional traffic detection methods. Current approaches either fail to fully exploit the raw characteristics of traffic or do not consider the timeliness requirements of UAV networks. To address these challenges, we propose a novel twin graph encoder neural network, which can extract features of raw traffic bytes for efficient traffic detection. First, we propose a decoupled architecture for model training and inference to enable efficient detection of malicious traffic in UAV networks. Second, we propose a novel modeling method that models traffic as the co-occurrence graph and word frequency graph based on raw bytes. Then, we propose TGE-ETD, a Twin Graph Encoder for Encrypted Traffic Detection. TGE-ETD consists of a set of twin graph encoders that effectively extract intrinsic traffic features from graphs constructed from raw bytes. In addition, TGE-ETD employs a global attention pooling mechanism to effectively distinguish the feature contributions of different bytes. Finally, we conducted extensive experiments on a real UAV traffic dataset and four real-world network traffic datasets. TGE-ETD achieved an improvement of 1%-20% over the baseline methods by reducing the number of parameters by 20 times. Tested on multiple UAV hardware devices, TGE-ETD can achieve millisecond-level traffic detection.

In an open and complex environment of drone service collaboration scenarios, efficient identity authentication is essential for secure service interactions. Traditional schemes suffer from high computational costs, low communication efficiency, and vulnerability to single-point failures. This paper presents a new lightweight authentication scheme (BDP-Auth) based on blockchain decentralized identity (DID) and physically unclonable functions (PUFs). PUF enables low-power, low-cost identity key generation without relying on complex computation or large storage, ensuring tamper-proof identifiers while reducing hardware and energy overhead. Using blockchain decentralization, the DID mechanism securely registers and stores drone identities, offering transparency and integrity. Security analysis shows that BDP-Auth achieves forward and backward security, meaning that the compromise of the current session key does not reveal past or subsequent session communications, and also resists physical capture and impersonation attacks. Experiments demonstrate that, compared to PRLAP-IoD, BDP-Auth reduces the computation overhead by 38.3% and 45.0% on the STM32F4 and NodeMCU platforms, respectively, decreases communication overhead by 36.9%, and reduces storage overhead by 27.5%, significantly improving overall efficiency.

Backdoor attacks pose a serious and emerging threat to deep neural networks. By injecting trigger-labeled samples into the training data, these attacks cause compromised models to misclassify any input containing the trigger. Existing defenses typically rely on either recovering the trigger to remove the corresponding backdoor or performing additional fine-tuning to erase the backdoor functionality while retaining the legitimate functionality. Both strategies require access to a trusted clean dataset, an assumption that is often unrealistic in practice. We introduce Double-Blind Cleanser (DBC), a backdoor unlearning framework that eliminates the need for trigger recovery or poisoned sample identification. DBC operates in two phases. It first leverages catastrophic forgetting to eliminate the backdoor behavior. It then applies Sharpness-Aware Minimization (SAM) to flatten the loss landscape, improving generalization and restoring legitimate functionality without requiring prior knowledge of triggers or poisoned samples. To further enhance the flatness of the loss landscape, DBC employs an importance sampling strategy that prioritizes samples most sensitive to weight perturbations, effectively reducing the impact of poisoned instances. Extensive evaluations demonstrate that DBC neutralizes diverse backdoor attacks while maintaining model utility. Its superior performance achieved first place in the mitigation round of the TrojAI competition, a multi-year, multi-round benchmark for backdoor defense.

Data valuation provides a principled framework for quantifying the contribution of data to model training. It plays a crucial role in trustworthy machine learning (ML) by supporting data curation, enhancing interpretability, and enabling fair incentive mechanisms in data markets. Shapley value is a popular method for data valuation, but accurate estimation remains computationally expensive, particularly at the dataset level. In this paper, we introduce Ensemble Shapley, an efficient framework tailored for dataset-level valuation on the sharded structure. To reduce the computational costs, we propose a two-phase estimation method that apportions the intensive contribution computation costs across disjoint data shards and strategically reuses the computation results, achieving efficient contribution evaluation through the ensemble of shard models. However, weak shard models trained on noisy data may degrade ensemble models’ performance. To solve this, we introduce a behavior-driven guided sampling method that pairs noisy datasets with benign ones, ensuring reliable contribution estimates despite the noise. We also derive an advantageous lower bound for the number of evaluation iterations that balances efficiency and accuracy by the number of shards. Experimental results show Ensemble Shapley has superior efficiency over existing methods while maintaining comparable accuracy across various ML tasks, and demonstrates strong scalability and integration potential.

Federated learning significantly enhances immersive user experiences in the human-centric metaverse by training machine learning models while keeping distributed data localized. As the right to be forgotten is legislated globally, users in the human-centric metaverse should have the option to have their data forgotten. Federated Unlearning (FU) is an emerging solution that enables the server to remove client contributions from trained global models by calibrating the historical model parameters of participating clients. However, retaining the entire model parameters incurs significant storage overhead, and the involvement of the remaining clients exposes the global model to the risk of backdoor attacks. This paper presents a new parameter-efficient and backdoor-resistant FU framework, called FedHydra. FedHydra segments the model into a base and a classifier layer. By storing the classifier parameters critical to predicting output, FedHydra reduces the storage overhead of the server. FedHydra also calibrates the classifier parameters and compares cosine similarity between the old and new classifier parameters of the remaining clients, reducing the computational overhead of unlearning while effectively preventing the abnormal clients from participating in the calibration. Experiments show that, compared with the full parameter-based storage calibration method, FedHydra achieves comparable unlearning effects, re duces storage overhead on the server, and improves unlearning efficiency. In addition, FedHydra reduces the attack success rates in different backdoor attack settings while maintaining the predictive performance of the global model.

The open-source software (OSS) ecosystem suffers from security threats caused by malware. However, OSS malware research has three limitations: a lack of high-quality datasets, a lack of malware diversity, and a lack of attack campaign contexts. In this paper, we first build the largest dataset of 24,356 malicious packages from online sources, then propose a knowledge graph to represent the OSS malware corpus and conduct malware analysis in the wild. Our main findings include (1) it is essential to collect malicious packages from various online sources because their data overlapping degrees are small; (2) despite the sheer volume of malicious packages, many reuse similar code, leading to a low diversity of malware; (3) only 28 malicious packages were repeatedly hidden via dependency libraries of 1,354 malicious packages, and dependency-hidden malware has a shorter active time; (4) security reports are the only reliable source for disclosing the malware-based context.

The large number of devices and complex communication requirements pose challenges to ensuring the security of Non-Terrestrial Internet of Things (NT-IoT). The large-scale data and complex communication requirements make accurate detection of malicious traffic even more challenging in NT-IoT. Hypergraph neural networks have strong performance in extracting multi-relational features. However, most existing hypergraph neural networks are tailored for graph data, and hyperedge construction methods are not well-suited. To address these challenges, we propose a malicious encrypted traffic detection method based on a hypergraph neural network. First, we propose an efficient hypergraph construction method for encrypted traffic named JointKNN. JointKNN calculates the Euclidean distance between traffic flows and adds the target nodes into the neighbor sets to form the hyperedges. Then, we propose an Encrypted Traffic HyperGraph Convolution Network (ETHGCN), which takes the encrypted traffic hypergraph as the input. ETHGCN extracts and fuses both connection and temporal features to accurately detect malicious traffic. We conduct comparative experiments on IoT and Onion Network encrypted traffic datasets for multi-class and binary classification tasks. Results indicate that ETHGCN achieves an accuracy exceeding 99.8% in IoT tasks and demonstrates an improvement of nearly 20% in Onion Network tasks.

Leveraging Large Language Models (LLMs) to write policy code for controlling robots has gained significant attention. However, in long-horizon implicative tasks, this approach often results in API parameter, comments and sequencing errors, leading to task failure. To address this problem, we propose a collaborative Triple-S framework that involves multiple LLMs. Through In-Context Learning, different LLMs assume specific roles in a closed-loop Simplification-Solution-Summary process, effectively improving success rates and robustness in long-horizon implicative tasks. Additionally, a novel demonstration library update mechanism which learned from success allows it to generalize to previously failed tasks. We validate the framework in the Long-horizon Desktop Implicative Placement (LDIP) dataset across various baseline models, where Triple-S successfully executes 89% of tasks in both observable and partially observable scenarios. Experiments in both simulation and real-world robot settings further validated the effectiveness of Triple-S. Our code and dataset is available at: https://github.com/Ghbbbbb/Triple-S.

With the proliferation of data sharing in the Internet of Things (IoT), protecting privacy-sensitive information and preventing unauthorized access have become paramount concerns. Existing centralized access control methods faces single-point-of-failure risks and lacks scalability for dynamic IoT systems. This paper proposes a fine-grained access control framework based on cross-blockchain technology for transparent and flexible IoT data sharing. In our framework, the cross-blockchain module is facilitated to eliminate data isolation across domains, the Interplanetary File System (IPFS) is used to mitigate centralized storage risks and reduce blockchain storage overhead, CP-ABE and symmetric encryption are integrated to enforce attribute-based, fine-grained access control with strong security guarantees. Meanwhile, the blockchain records data and key references to enforce secure access to shared data content. We further conduct security analysis and experimental evaluations, demonstrating the effectiveness and efficiency of the proposed scheme.

Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.

Considerable efforts have been devoted to addressing distributed backdoor attacks in federated learning (FL) systems. While significant progress has been made in enhancing the security of FL systems, our study reveals that there remains a false sense of security surrounding FL. We demonstrate that colluding malicious participants can effectively execute backdoor attacks during the FL training process, exhibiting high sparsity and stealthiness, which means they can evade common defense methods with only a few attack iterations. Our research highlights this vulnerability by proposing aCollusiveBackdoorAttack namedCoBA.CoBAis designed to enhance the sparsity and stealthiness of backdoor attacks by offering trigger tuning to facilitate learning of backdoor training data, controlling the bias of malicious local model updates, and applying the projected gradient descent technique. By conducting extensive empirical studies on 5 benchmark datasets, we make the following observations: 1)CoBAsuccessfully circumvents 15 state-of-the-art defense methods for robust FL; 2) Compared to existing backdoor attacks,CoBAconsistently achieves superior attack performance; and 3)CoBAcan achieve persistent poisoning effects through significantly sparse attack iterations. These findings raise substantial concerns regarding the integrity of FL and underscore the urgent need for heightened vigilance in defending against such attacks.

Despite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be vulnerable to adversarial attacks. White-box and black-box adversarial ML attacks of NIDS have been explored in several studies. However, white-box attacks unrealistically assume that the attackers have full knowledge of the target NIDSs. Meanwhile, existing black-box attacks can not achieve high attack success rate due to the weak adversarial transferability between models (e.g., neural networks and tree models). Additionally, neither of them explains why adversarial examples exist and why they can transfer across models. To address these challenges, this paper introduces ETA, anExplainableTransfer-based Black-Box AdversarialAttack framework. ETA aims to achieve two primary objectives: 1) create transferable adversarial examples applicable to various ML detectors and 2) provide insights into the existence of adversarial examples and their transferability within NIDSs. Specifically, we first provide a general transfer-based adversarial attack method applicable across the entire ML space. Following that, we exploit a unique insight based on cooperative game theory and perturbation interpretations to explain adversarial examples and adversarial transferability. On this basis, we propose an Important-Sensitive Feature Selection (ISFS) method to guide the search for adversarial examples, achieving stronger transferability and ensuring traffic-space constraints. Finally, the experimental results on three NIDSs datasets show that our method performs significantly effectively against several classical and state-of-the-art ML classifiers, outperforming the latest baselines. We conduct three interpretation experiments and two cases to verify our interpretation method's correctness. Meanwhile, we uncover two major misconceptions about applying machine learning to NIDSs systems.

In cooperative intelligent transportation systems (CITS), federated learning enables vehicles to train a global model without sharing private data. However, the lack of an unlearning mechanism to remove the influence of vehicle-specified data from the global model potentially violates data protection regulations regarding the right to be forgotten. While the existing federated unlearning (FU) methods exhibit promising unlearning effects, their practicality in CITS is hindered due to the time-consuming retraining steps required by other vehicles and the non-negligible performance sacrifice on the un-forgotten data. Therefore, achieving effective unlearning without extensive retraining, while minimizing performance degradation on the un-forgotten data remains a challenge. In this work, we propose FedEditor, an efficient and effective FU framework in CITS that addresses the above challenge by reconfiguring the global model’s representation space to remove critical classification-related knowledge from the unlearned data. Firstly, FedEditor enables vehicles to perform the unlearning process locally on the global model, eliminating the participation of other vehicles and improving efficiency. Secondly, FedEditor captures and aligns the representations of the unlearned data with those of the nearest incorrect class centroid derived from non-training data, ensuring effective unlearning while preserving the un-forgotten data’s knowledge relatively intact for achieving competitive model performance. Finally, FedEditor refines the global model’s output distributions using the vehicles’ remaining data and incorporates a drift-mitigating regularization term, minimizing the negative impact of unlearning operations on model performance. Experimental results show that FedEditor reduces the unlearning rate by up to 99.64% without time-consuming retraining, while limiting the predictive performance loss of the resulting global model to less than 3.88% across five models and seven datasets.

A fundamental goal of Web 3.0 is to establish a decentralized network and application ecosystem, thereby enabling users to retain control over their data while promoting value exchange. However, the recent TRON-Steem takeover incident poses a significant threat to this vision. In this paper, we present a thorough empirical analysis of the TRON-Steem takeover incident. By conducting a fine-grained reconstruction of the stake and election snapshots within the Steem blockchain, one of the most prominent social-oriented blockchains, we quantify the marked shifts in decentralization pre and post the takeover incident, highlighting the severe threat that blockchain network takeovers pose to the decentralization principle of Web 3.0. Moreover, by employing heuristic methods to identify anomalous voters and conducting clustering analyses on voter behaviors, we unveil the underlying mechanics of takeover strategies employed in the TRON-Steem incident and suggest potential mitigation strategies, which contribute to the enhanced resistance of Web 3.0 networks against similar threats in the future. We believe the insights gleaned from this research help illuminate the challenges imposed by blockchain network takeovers in the Web 3.0 era, suggest ways to foster the development of decentralized technologies and governance, as well as to enhance the protection of Web 3.0 user rights.

Federated recommendation (FR) is a decentralised approach to training personalised recommender systems, protecting users' privacy by avoiding data collection. Despite its privacy advantages, FR remains vulnerable to poisoning attacks. We focus on untargeted poisoning attacks against FR which degrade the overall performance of recommender services, leading to a detrimental impact on user experience and service quality. In this paper, we propose a general framework to formalise untargeted attacks and identify the vital role played by the interplay between items and user profiles in determining FR's performance. We present an untargeted attack FRecAttack2 which exploits this interplay. Specifically, we develop various methods for sampling user profiles, which approximate user distributions with and without collusion among malicious users. Then we leverage a new measurement to identify items that can disrupt the original interplay with user profiles, based on the change velocity of items' recommendation scores during optimisation. Extensive experiments demonstrate the superiority of our attack, outperforming existing methods by up to 27.56%, and its stealthiness in evading mainstream defences. To counteract untargeted attacks, we present a defence GuardCQ to detect malicious users by quantifying their contribution to boost the right interplay between items and user profiles. Empirical results show that GuardCQ effectively mitigates the attack's impact on FR and enhances the robustness of FR against poisoning attacks.

The development of LEO satellite networks (LSN) makes them a potential solution to deliver broadcast/multicast traffic to deploy and upgrade massive amounts of Internet of Things (IoT) devices in future 6G networks. However, inherent resource constraints of LSN leave them vulnerable to a multitude of security threats, most notably distributed denial-of-service (DDoS) attacks. Existing solutions are primarily based on machine learning detection methods which are incapable of defending against unknown zero-day attacks. This paper presents an innovative solution leveraging deep reinforcement learning (DRL) to create a dynamic multicast tree based on moving target defense (MTD), aimed at enhancing the security of multicast services in LSN. The proposed solution adopts an adaptive orbital tree mutation (AOTM) scheme that dynamically adjusts multicast tree configurations considering quality of service (QoS) constraints to avoid attacks on vulnerable nodes. Simulations demonstrate the effectiveness of the AOTM scheme, showcasing its superior defense success rates compared to existing state-of-the-art algorithms.

This paper proposes a novel symbolic execution path exploration strategy named MS-ALS (Multi-strategy Active Learning Search). MS-ALS integrates multiple heuristic methods and introduces a machine learning model to learn symbolic states of program paths from the training set, aiming to predict the reward of symbolic states of program paths for selecting the optimal states. To obtain an accurate predictive model, this paper employs an active learning approach based on multiple query strategies. It selects symbolic states of program paths with high uncertainty, representativeness, and low redundancy from the pool of states to annotate, feeding back the state samples to the model to guide it towards more accurate predictions. This enables symbolic execution tools to explore input programs more efficiently. Experiments show that MS-ALS achieves higher code coverage and identifies more security violations compared to baseline methods. Additionally, test cases generated by MS-ALS also exhibit higher quality, improving AFL’s path discovery in fuzz testing when used as initial seeds.

With the advent of the 5.5G era, integrated sensing and communication (ISAC) technology has emerged, demonstrating its high-precision sensing capabilities across industries like wireless communication, intelligent transportation, and smart homes. Researchers are particularly exploring the use of channel state information (CSI) from WiFi signals for human activity recognition. However, current research primarily relies on complex network models to manage vast CSI data for developing accurate recognition models. It confronts two main challenges in time-sensitive applications: a limited number of samples and the need for timely solutions. In this paper, we present a prototype network model that combines CNN and self-attention to address the issues raised above, namely Proto-CSNet. First, we use time-frequency analysis and filter the data to transfer the data from the time domain to the frequency domain and eliminate noise and outliers from the samples. Then, based on the ACmix model, we modify and build a novel custom feature extraction module that retains the fused CNN's local feature capture capabilities as well as self-attention's global information processing advantage. Finally, we merge the custom modules with CNNs to form a prototype network. A real-world dataset is constructed including 11 different human activities, and the extensive experimental resutls demonstrate that Proto-CSNet outperforms existing algorithms with a model inference accuracy exceeding 95%.

Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi‐Actor‐Attention‐Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single‐vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.

5G and Beyond 5G communication technologies have promoted the architectural innovation of the Industrial Internet of Things (IIoT) and the wide application of edge learning. As Beyond 5G technologies enhance wireless communication within IIoT, the demand for efficient, secure data management becomes paramount. Edge learning emerges as a solution for localized model training, reducing the necessity for extensive data transmission. However, this decentralization introduces vulnerabilities, particularly in data security during transmission and efficient resource utilization. To address the challenges of data scheduling for edge learning in the Wireless IIoT (WIIoT), we propose a novel architecture that leverages blockchain for secure, decentralized data scheduling and employs physically unclonable functions (PUFs)-based algorithm to ensure data integrity and confidentiality. The primary contributions consist of a task scheduling model based on blockchain, along with a data compression scheme in multiple stages combined with a data scheduling algorithm that is optimized for energy efficiency in edge learning environments. Experiments conducted on a simulated WIIoT platform comprising embedded devices validate our approach, demonstrating enhanced data security and learning efficiency which can reduce 40% in the training stage and 70% in the inference stage. Our findings contribute to the advancement of security and efficient edge learning frameworks in the context of WIIoT, addressing the intricate balance between security, efficiency, and decentralized trust.

In recent years, electric vehicles (EVs) have emerged as a promising mode of transportation. With the development of Internet of Robotic Things (IoRT) technology, charging stations are employing interconnected robots to charge EVs, automating the collection and transmission of user charging information. However, charging processes pose risks of privacy leakage to users, as malicious attackers could potentially exploit the collected charging information to infer the real identities and behavioral habits of EV users. Existing studies leverage the decentralization and anonymity of blockchain to achieve privacy-preserving charging management. Due to the increasing number of users and limited battery capacity, there is a large volume of charging requests demand to be processed. However, the consensus mechanism of blockchain limits the system throughput. Therefore, it is a challenge to preserve the privacy of EV users and simultaneously improve the system processing efficiency. To address these concerns, we propose an efficient and privacy-preserving EV charging scheme (EPDB), which leverages decentralized identifier (DID) and Pedersen commitment scheme to achieve reliable charging reservations while hiding EV User’s charging information. Additionally, we propose an efficient blockchain consensus protocol, which serves as the underlying storage for DID, thus significantly improving the system throughput. Furthermore, our proposed consensus protocol maintains high throughput even when encountering Byzantine attacks. Our theoretical analysis indicates that EPDB scheme effectively mitigate Byzantine attacks, preserve privacy and prevents deception of charging services, and our experimental results demonstrate the high efficiency of EPDB scheme.

Nowadays security vulnerability reports contain commercial vendor-centric information but fail to include accurate information of open-source software packages. Open-source ecosystems use package managers, such as Maven, NuGet, NPM, and Gem, to cover hundreds of thousands of free code packages. However, we uncover that vulnerability reports frequently miss the vulnerable software package information when the software package comes from open-source ecosystems. To fill in this gap, we propose a framework called PKVIC (softwarepackagevulnerabilityinformationcalibration), as the first tool to automatically associate security vulnerability reports with affected software packages from different open-source ecosystems. Specifically, PKVIC designs an ecosystem classifier to determine which ecosystem a vulnerability report belongs to. From the reports written in natural language, PKVIC extracts the entities closely related to software names in ecosystems. To efficiently and accurately locate the affected software packages from millions of packages, we propose a recursive traversal method to generate the package identifier based on the naming scheme and candidate named entities. We implemented the prototype of PKVIC and conducted comprehensive experiments to validate its efficacy. In particular, we ran PKVIC over 421,808 vulnerability reports from 20 well-known sources of security vulnerabilities and identified 11,279 unique vulnerability reports that affected 2,703 open-source software packages. PKVIC successfully found the accurate reference URLs for these 2,703 software packages across 6 open-source ecosystems, including Pypi, Gem, NPM, Packagist, Nuget, and Maven.

Remote sensing change detection (RSCD) recognizes pixel-level change regions between images, while remote sensing change captioning (RSCC) describes the nature and properties of these changes in natural language. The past have studied two tasks individually. Resulting in a single interpretation content produced and limited application scenarios. And the complementarity of change regions and deep semantic information can further improve the performance and robustness of the model. Therefore, we try simultaneously to solve both RSCD and RSCC tasks (i.e., RS-CDC) under a multitask framework, namely a CNN-Transformer-based multitask network (CTMTNet). Specifically, we design multiattention feature enhancement module (MAFEM) and feature fusion block (FFB) to enhance local information and location perception of features from bitemporal images. The MAFEM weights the channel and space separately to capture local information more accurately and enhance location perception. The FFB fuses bitemporal features and uses multilevel residual connections to ensure that change information is not lost during transfer. Finally, we use two decoders to output the change maps (CMs) and change captioning, respectively. During training, we use an improved multitask loss function for CTMTNet to balance the two tasks. For exploring the RS-CDC task, we construct two large-scale datasets named LEVIR-CDC and WHU-CDC dataset. We benchmark the existing state-of-the-art (SOTA) change detection (CD) and change captioning methods on these two datasets and a newly publicized LEVIR-MCI dataset, and the results show that the proposed CTMTNet significantly outperforms comparative methods.

Object detection of remote sensing images is a fundamental and important task for a wide range of applications, such as agriculture, military, and geological exploration. However, it is still a challenging task due to complex background, heavy occlusion, and object ambiguities. Complex remote sensing scenes contain rich visual and spatial relationships between proposals, while most existing methods ignore these relationships. In this article, we introduce a novel double-head global reasoning network (DGRN), which endows an object detection model with the ability to adapt global reasoning by propagating visual and spatial embeddings between positive proposals (foreground) and negative proposals (background). Instead of propagating the features of positive proposals, we evolve high-level embeddings globally to explore potential information in all proposals. Specifically, our method first classifies and locates positive proposals and negative proposals simultaneously, and then adaptively learns two sparse region-to-region undirected graphs for classification and location, respectively. Finally, the graph reasoning module (GRM) conducts the propagation of proposals’ embedding to improve the performance of object classification and localization. Without any prior knowledge, our DGRN method explores reasoning graphs between proposals for object classification and location, respectively, and then uses graph convolutional neural network (GCN) to propagate information and achieve relational reasoning. Our method is light-weighted and flexible enough to enhance many object detection models, making object detection models the capability of relational reasoning. The experimental results on DOTA and DIOR showed that the proposed method had better detection performance than object detection networks without considering relationships.

Federated recommendation (FR) addresses privacy concerns in recommender systems by training a global model without requiring raw user data to leave individual devices. A server, known as the aggregator, integrates users’ local gradients and updates the global model parameters. However, FR is vulnerable to attacks where malicious users manipulate these updates, known as model poisoning attacks. In this work, we propose a new targeted attack calledStairClimbingto promote specific items through model poisoning, and a new defence mechanismCrossEU. StairClimbingadopts a new strategy resembling stair climbing to enable target items to beat competitive items and increase their popularity level by level. Compared to prior attacks,StairClimbingguarantees balanced effectiveness, efficiency and stealthiness simultaneously. Our defence mechanismCrossEUleverages two patterns regarding the lists of items updated by benign users between iterative epochs. Extensive experiments on six real-world datasets demonstrateStairClimbing’s superiority across all three desirable attack properties, even with a small proportion of malicious users (1%). In addition,CrossEUeffectively delays the impact of all tested attacks and even eliminates their damage entirely.

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to collaboratively train a global model for intelligent transportation without sharing their local data. However, due to dynamic network structure and unreliable wireless communication of VANET, various potential risks (e.g., identity privacy leakage, data privacy inference, model integrity compromise, and data manipulation) undermine the trustworthiness of intermediate model parameters necessary for building the global model. While existing cryptography techniques and differential privacy provide provable security paradigms, the practicality of secure federated learning in VANET is hindered in terms of training efficiency and model performance. Therefore, developing a secure and efficient federated learning in VANET remains a challenge. In this work, we propose a privacy-enhanced and efficient authentication protocol for federated learning in VANET, called FedComm. Unlike existing solutions, FedComm addresses the above challenge through user anonymity. First, FedComm enables vehicles to participate in training with unlinkable pseudonyms, ensuring both privacy preservation and efficient collaboration. Second, FedComm incorporates an efficient authentication protocol to guarantee the authenticity and integrity of model parameters originated from anonymous vehicles. Finally, FedComm accurately identifies and completely eliminates malicious vehicles in anonymous communication. Security analysis and verification with ProVerif demonstrate that FedComm enhances privacy and reliability of intermediate model parameters. Experimental results show that FedComm reduces the overhead of proof generation and verification by 67.38% and 67.39%, respectively, compared with the state-of-the-art authentication protocols used in federated learning.

Train delay prediction is a key technology for intelligent train scheduling and passenger services. We propose a train delay prediction model that takes into account the asynchrony of train events, the dynamics of train operations, and the diversity of influencing factors. Firstly, we consider train operations as discrete sequences of train events and propose a train arrival neural temporal point process (TANTPP) framework focused on predicting train delays that explicitly models the asynchrony of train events. Secondly, we introduce a multi-source dynamic spatiotemporal embedding method for the feature encoder in the TANTPP framework, which enhances the capability to capture the features of train operation networks. Thirdly, to better capture the distribution of train events in the TANTPP framework, we utilize a log-normal mixture hybrid method to learn the probability density distribution of train arrival events. Finally, the experimental result on real-world datasets demonstrates that the TANTPP model outperforms current state-of-the-art models, reducing the MAE by 10.85%, the RMSE by 9.8%, the RRSE by 3.78% and the MAPE by 10.11% on average. To the best of our knowledge, this is the first study to utilize neural temporal point processes to enhance train delay prediction.

Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data. However, deploying FL frameworks in real IoT scenarios faces three critical challenges, i.e., statistical heterogeneity, resource constraint, and fairness. To address these challenges, we design a fair and efficient FL method, termed FedASA, which can address the challenge of statistical heterogeneity in resource-constrained scenarios by determining the shared architecture adaptively. In FedASA, we first present a cell-wised shared architecture selection strategy, which can adaptively construct the shared architecture for each device. We then design a cell-based aggregation algorithm for aggregating heterogeneous shared architectures. In addition, we provide a theoretical analysis of the federated error bound, which provides a theoretical guarantee for the fairness. At the same time, we prove the convergence of FedASA at the first-order stationary point. We evaluate the performance of FedASA through extensive simulation and experiments. Experimental results in cross-location scenarios show that FedASA outperformed the state-of-the-art approaches, improving accuracy by up to 13.27% with better fairness and faster convergence and communication requirement has been reduced by 81.49%.

Indoor localization availability is still sporadic in industry, especially at the black-hole, i.e., there only exist cellular signals, no GPS or WiFi signals. Based on our 2-year observations at the DiDi ride-hailing platform in China, there are$ 68\,\text{k}$orders everyday created at black-hole. In this paper, we presentTransparentLoc, a large-scale cellular localization system for pickup position recommendation of the DiDi platform. Specifically, we design a CNN model for real-time localization based on a crowdsourcing fingerprint set constructed by outdoor trajectories and abnormal cell tower detection. Then we leverage a DeepFM model to recommend an optimal pickup position for passengers. We share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, temporal variations, and abnormal cell towers in terms of four major service metrics, i.e., pickup position error, over-30-meters ratio, cancel ratio, and call ratio. The large-scale evaluations show that our system achieves a$ 0.54\,\text{m}$lower median pickup position error compared to the iOS built-in cellular localization system, regardless of environmental changes, smartphone brands/models, time, and cellular providers. Additionally, the over-30-meters ratio, cancel ratio, and call ratio have significant reductions of 0.88%, 0.88%, and 5.13%, respectively.

With the increasing number of Internet of Things (IoT) devices connected to the internet, the industry and research community have become increasingly concerned about their security impact. Adversaries or hackers often exploit public security flaws to compromise IoT devices and launch cyber attacks. However, despite this growing concern, little effort has been made to investigate the detection of IoT devices and their underlying risks. To address this gap, this article proposes to automatically establish relationships between IoT devices and their vulnerabilities in the wild. Specifically, we construct a deep neural network (DNN) to extract semantic information from IoT packets and generate fine-grained fingerprints of IoT devices. This enables us to annotate IoT devices in cyberspace, including their device type, vendor, and product information. We collect vulnerability reports from various security sources and extract IoT device information from these reports to automatically match vulnerabilities with the fingerprints of IoT devices. We implemented a prototype system and conducted extensive experiments to validate the effectiveness of our approach. The results show that our DNN model achieved a 98% precision rate and a 95% recall rate in IoT device fingerprinting. Furthermore, we collected and analyzed over 13,063 IoT-related vulnerability reports and our method automatically built 5,458 connections between IoT device fingerprints and their vulnerabilities. These findings shed light on the ongoing threat of cyber-attacks on IoT systems as both IoT devices and disclosed vulnerabilities are targets for malicious attackers.

Recent advances in deep learning (DL) have brought tremendous gains in signal modulation classification. However, DL-based classifiers lack transparency and interpretability, which raises concern about model's reliability and hinders the wide deployment in real-word applications. While explainable methods have recently emerged, little has been done to explain the DL-based signal modulation classifiers. In this work, we propose a novel model-agnostic explainer, Model-Agnostic Signal modulation classification Explainer (MASE), which provides explanations for the predictions of black-box modulation classifiers. With the subsequence-based signal interpretable representation and in-distribution local signal sampling, MASE learns a local linear surrogate model to derive a class activation vector, which assigns importance values to the timesteps of signal instance. Besides, the constellation-based explanation visualization is adopted to spotlight the important signal features relevant to model prediction. We furthermore propose the first generic quantitative explanation evaluation framework for signal modulation classification to automatically measure the faithfulness, sensitivity, robustness, and efficiency of explanations. Extensive experiments are conducted on two real-world datasets with four black-box signal modulation classifiers. The quantitative results indicate MASE outperforms two state-of-the-art methods with 44.7% improvement in faithfulness, 30.6% improvement in robustness, and 44.1% decrease in sensitivity. Through qualitative visualizations, we further demonstrate the explanations of MASE are more human interpretable and provide better understanding into the reliability of black-box model decisions.

Background:Multi-source cross-project defect prediction (MSCPDP) attempts to transfer defect knowledge learned from multiple source projects to the target project. MSCPDP has drawn increasing attention from academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP). However, two main problems, which are how to effectively extract the transferable knowledge from each source dataset and how to measure the amount of knowledge transferred from each source dataset to the target dataset, seriously restrict the performance of existing MSCPDP models.Objective:In this paper, we propose a novel multi-source transfer weighted ensemble learning (MASTER) method for MSCPDP.Method:MASTER measures the weight of each source dataset based on feature importance and distribution difference and then extracts the transferable knowledge based on the proposed feature-weighted transfer learning algorithm. Experiments are performed on 30 software projects. We compare MASTER with the latest state-of-the-art MSCPDP methods with statistical test in terms of famous effort-unaware measures (i.e., PD, PF, AUC, and MCC) and two widely used effort-aware measures (Popt20% and IFA).Result:The experiment results show that: 1) MASTER can substantially improve the prediction performance compared with the baselines, e.g., an improvement of at least 49.1% in MCC, 48.1% in IFA; 2) MASTER significantly outperforms each baseline on most datasets in terms of AUC, MCC,Popt20% and IFA; 3) MSCPDP model significantly performs better than the mean case of SSCPDP model on most datasets and even outperforms the best case of SSCPDP on some datasets.Conclusion:It can be concluded that 1) it is very necessary to conduct MSCPDP, and 2) the proposed MASTER is a more promising alternative for MSCPDP.

Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal of various defense strategies deployed? This is an intriguing problem with significant practical implications regarding the utility of FL services. Despite the recent flourish of poisoning-resilient FL methods, our study shows that carefully tuning the collusion between malicious participants can minimize the trigger-induced bias of the poisoned local model from the poison-free one, which plays the key role in delivering stealthy backdoor attacks and circumventing a wide spectrum of state-of-the-art defense methods in FL. In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). It jointly tunes the backdoor trigger and controls the poisoned model changes on each malicious participant to achieve a stealthy yet successful backdoor attack against a wide spectrum of defensive mechanisms of federated learning techniques. Our extensive study on 3 large-scale benchmark datasets and 13 mainstream defensive mechanisms confirms that Cerberus Poisoning raises a significantly severe threat to the integrity and security of federated learning practices, regardless of the flourish of robust Federated Learning methods.

Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers.

With the popularization of emerging technologies such as artificial intelligence, 5G and beyond, extended reality and blockchain, the next generation Internet is rapid expansion. “Metaverse” as an evolving paradigm of next-generation Internet, can be recognized as a fully immersive, hyper spatiotemporal and self-sustaining virtual shared space, and its concept is continuous development and evolution. It is moving from imagination to the coming reality, but it is still far from being realized. One of reasons is that distinct sub-metaverses deploying their services on heterogeneous blockchains results in major problems for interoperability, preventing the implementation of seamless integrated metaverse. Facing the challenge, this paper proposes a decentralized identifiers (DIDs) assisted secure cross-metaverse authentication scheme for MEC-enabled metaverse, which is based on a novel designed infrastructure build on MEC and blockchain. In addition, the proposed scheme adopts DIDs, which can not only achieve the secure cross-metaverse authentication, but also increase the decentralization of the metaverse. In addition, the adoption of ID-based aggregate signature can reduce the overhead of computation, communication and storage.

Robust reinforcement learning (RL) has been a challenging problem due to the gap between simulation and the real world. Existing efforts typically address the robust RL problem by solving a max-min problem. The main idea is to maximize the cumulative reward under the worst-possible perturbations. However, the worst-case optimization either leads to overly conservative solutions or unstable training process, which further affects the policy robustness and generalization performance. In this paper, we tackle this problem from both formulation definition and algorithm design. First, we formulate the robust RL as a max-expectation optimization problem, where the goal is to find an optimal policy under both the worst cases and the non-worst cases. Then, we propose a novel framework DRRL to solve the max-expectation optimization. Given our definition of the feasible tasks, a task generation and sequencing mechanism is introduced to dynamically output tasks at appropriate difficulty level for the current policy. With these progressive tasks, DRRL realizes dynamic multi-task learning to improve the policy robustness and the training stability. Finally, extensive experiments demonstrate that the proposed method exhibits significant performance on the unmanned CarRacing game and multiple high-dimensional MuJoCo environments.

Location awareness is the basis for enabling pickup service at ride-hailing platforms. In contrast to the almost pervasive coverage outdoors, indoor localization availability is still sporadic in industry since it largely relies on RF signatures from certain IT infrastructure, e.g., WiFi access points. Based on our 2-year observations at DiDi ride-hailing platform in China, there are 68k orders everyday created at black-hole, i.e., where only cellular signals exist. In this paper, we present the design, development, and deployment of TransparentLoc, a large-scale cellular localization system for pickup position recommendation, and share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, and temporal variations. Our system outperforms the iOS built-in cellular localization system in terms of four major service metrics, regardless of environmental changes, smartphone brands/models, time, and cellular providers.

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.

High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.

The hardness of discrete logarithm problem (DLP) over finite fields is the security foundation of many cryptographic protocols. When the characteristic is not small, the state-of-the-art algorithms for solving DLP are the number field sieve (NFS) and its variants. In the relation collection step, to translate the relations between prime ideals to those of elements one needs to use the Schirokauer map. Besides, if the number field has non-trivial automorphisms, one can use them to accelerate the factor-base logarithms computation. However, the Schirokauer map is not compatible with automorphisms. To exploit automorphism efficiently, we focus on the method to construct fields in NFS such that the fields on both sides have non-trivial automorphisms with the logarithms of units being zero: 1) we construct two families of CM polynomials of arbitrary even degree with small coefficients, corresponding to the automorphisms being$x\mapsto -x$or$x\mapsto 1/x$; 2) we show how to combine these polynomials with the JLSV1 and Conjugation polynomial selection methods on both sides; and 3) we also generalize our method to the multiple number field sieve and the extended tower number field sieve.

Maritime ships and ports have become increasingly digital and intelligent. While intelligent maritime transportation systems bring convenience to the maritime industry, ship operation and management are also confronted with network risks. The Internet of Things (IoT) installed in the shipborne network collects and monitors the environmental data of the whole ship. It uses the collected data to make decisions to control the ship. The threat of Local Area Network (LAN) of IoT in ships has become an emerging issue. The DNS rebinding attack is a typical attack, which can bypass firewalls and seriously threaten the marine network in security and privacy of the local IoT. DNS rebinding attacks are difficult to model and detect, due to their sophisticated characteristics. In this work, we define threat models of DNS rebinding attacks and propose an effective method for the detection of and the defense against these attacks. First, we define threat models for DNS rebinding attacks. We employ a Markov chain to model the process of DNS rebinding attacks. With the threat modeling, the attack behaviors are clearly characterized and the most relevant attributes are thus extracted. Second, we propose an effective method for the detection of DNS rebinding attacks in the marine transportation system. The detection method includes the initialization method and the verification method, which manages and verifies access permission of equipment information and the service interface of the IoT in the shipborn network. Finally, we simulate the DNS rebinding attacks on the marine IoT. We analyze and test the security and the performance of the initialization method and the verification method in the simulated environment. The extensive experimental results demonstrate that the IoT in marine networks is vulnerable to DNS rebinding. Our method is effective and efficient to detect and defend against DNS rebinding attacks. It thus secures security and privacy in the local IoT on shipboard.

HotStuff is a state-of-the-art Byzantine fault tolerant (BFT) consensus which achieves both linear view change and responsiveness. Furthermore, Fast-HotStuff reduces one round of communication based on HotStuff for lower latency and can avoid forking attacks. However, there are some blocks which have obtained one or two rounds of voting cannot be committed due to attacks by the primary node, thus resulting in a waste of system resources and throughput drops. After analyzing possible attacks in HotStuff and Fast-HotStuff, we propose a new consensus protocol named Efficient-HotStuff, which guarantees that each block which has obtained a round of voting can be committed, hence it is more robust against attacks by the primary node. Our experimental results show that, compared with HotStuff and Fast-HotStuff, Efficient-HotStuff has the highest throughput and the lowest latency without attacks, and Efficient-HotStuff can maintain a much higher throughput than them when attacked by Byzantine primary nodes.

The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples—the legitimate input added with imperceptible and well-designed perturbation that can fool DNNs easily in the DNN testing/deploying stage. Previous white-box adversarial example generation algorithms used the Jacobian gradient information to add the perturbation. This imprecise and inexplicit information can cause unnecessary perturbation when generating adversarial examples. This paper aims to address this issue. We first propose to apply the more informative and distilled gradient information, namely, integrated gradient, to generate adversarial examples. To further make the perturbation more imperceptible, we propose to employ the restriction combination of L 0 and L 1 / L 2 second, which can restrict the total perturbation and the perturbation points simultaneously. Meanwhile, to address the nondifferentiable problem of L 1 , we explore a proximal operation of L 1 third. On the basis of these three works, we propose two Integrated gradient-based White-box Adversarial example generation algorithms (IWA): Integrated gradient-based Finite Point Attack (IFPA) and Integrated gradient-based Universe Attack (IUA). IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured data sets, and compare them with five baseline generation algorithms. The results show that our proposed algorithms craft adversarial examples with more imperceptible perturbation and satisfactory crafting rate. L 2 restriction is suitable for unstructured data sets and L 1 restriction performs better in the structured data set.

In the era of big data with exponential growth in data volume, how to reduce data security issues such as data leakage caused by machine learning is a hot area of recent research. The existing privacy budget allocation strategies are usually only suitable for data applications in specific spaces and cannot meet users' personalized needs for privacy budget allocation. Therefore, a linear privacy budget allocation strategy is proposed. The strategy assigns each layer a linearly increasing privacy budget from the root of the decision tree to the bottom by adjusting the coefficient or constant term. Combining this strategy with the random forest algorithm, a random forest algorithm based on linear privacy budget allocation (DiffPRF_linear) is formed. Experimental results show that the proposed algorithm can realize uniform, arithmetic, and geometric privacy budget allocation policy effects and can also achieve better classification effects than the former, which not only meets the needs of users to protect private data personalized but also maintains high classification accuracy.

An increasing number of embedded devices are connecting to the Internet, ranging from cameras, routers to printers, while an adversary can exploit security flaws already known to compromise those devices. Security patches are usually associated with the device firmware, which relies on the device vendors and products. Due to compatibility and release-time issues, many embedded devices are still using outdated firmware with known vulnerabilities or flaws. In this article, we conduct a systematic study on device vulnerabilities by leveraging firmware fingerprints. Specifically, we use a web crawler to gather 9,716 firmware images from official websites of device vendors, and 347,685 security reports scattered across data archives, blogs, and forums. We propose to generate fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. Furthermore, machine learning algorithms and regex are used to identify device vulnerabilities and corresponding device firmware fingerprints. We perform real-world experiments to validate the performance of the firmware fingerprint, which yields high accuracy of 91% precision and 90% recall. We reveal that 6,898 reports have the firmware and related vulnerability information, and there are more than 10% of firmware vulnerabilities without any patches or solutions for mitigating underlying security risks.

Since big data becomes a main impetus to the next generation of IT industry, data privacy has received considerable attention in recent years. To deal with the privacy challenges, differential privacy has been widely discussed and related private mechanisms are proposed as privacy-enhancing techniques. However, with today’s differential privacy techniques, it is difficult to generate a sanitized dataset that can suit every machine learning task. In order to adapt to various tasks and budgets, different kinds of privacy mechanisms have to be implemented, which inevitably incur enormous costs for computation and interaction. To this end, in this article, we propose two novel schemes for outsourcing differential privacy. The first scheme efficiently achieves outsourcing differential privacy by using our preprocessing method and secure building blocks. To support the queries from multiple evaluators, we give the second scheme that employs a trusted execution environment to aggregately implement privacy mechanisms on multiple queries. During data publishing, our proposed schemes allow providers to go off-line after uploading their datasets, so that they achieve a low communication cost which is one of the critical requirements for a practical system. Finally, we report an experimental evaluation on UCI datasets, which confirms the effectiveness of our schemes.

Quality of Service (QoS) guarantee is an important issue in building service-oriented applications. Generally, someQoSvalues of a service are unknown to its users who have never invoked the service before. Fortunately, collaborative filtering (CF)-based methods are proved feasible for missingQoSprediction and have been widely used. However, these methods seldom took the temporal factors into consideration. Indeed, historicalQoSvalues contain more information about user (or service) similarity. Furthermore, as the application environment is dynamic, obtainedQoSvalues usually have short timeliness. Hence, using outdatedQoSvalues will largely decrease the prediction accuracy. In order to resolve this issue, we proposed a time-aware collaborative filtering approach. First, we proposed aQoSmodel to filter out outdatedQoSvalues, and divided the obtainedQoSvalues into several time slices. Then, we computed the average value of historicalQoSas temporalQoSforecast. In addition, by introducing time-aware similarity computation mechanism, we succeeded to select real similar neighbor users (or services) and further predict theCF-basedQoSbased onCFtechnology. Finally, we can predict the final missingQoSby combining temporalQoSforecast andCF-basedQoSprediction. Experiment results show that our approach can receive better prediction precision.

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.

Reward functions are critical hyperparameters with commercial values for individual or distributed reinforcement learning (RL), as slightly different reward functions result in significantly different performance. However, existing inverse reinforcement learning (IRL) methods can be utilized to approximate reward functions just based on collected expert trajectories through observing. Thus, in the real RL process, how to generate a polluted trajectory and perform an adversarial attack on IRL for protecting reward functions has become the key issue. Meanwhile, considering the actual RL cost, generated adversarial trajectories should be minimal and non-catastrophic for ensuring normal RL performance. In this work, we propose a novel approach to craft adversarial trajectories disguised as expert ones, for decreasing the IRL performance and realize the anti-IRL ability. Firstly, we design a reward clustering-based metric to integrate both advantages of fine- and coarse-grained IRL assessment, including expected value difference (EVD) and mean reward loss (MRL). Further, based on such metric, we explore an adversarial attack based on agglomerative nesting algorithm (AGNES) clustering and determine targeted states as starting states for reward perturbation. Then we employ the intrinsic fear model to predict the probability of imminent catastrophe, supporting to generate non-catastrophic adversarial trajectories. Extensive experiments of 7 state-of-the-art IRL algorithms are implemented on the Object World benchmark, demonstrating the capability of our proposed approach in (a) decreasing the IRL performance and (b) having minimal and non-catastrophic adversarial trajectories.

Pathfinding becomes an important component in many real-world scenarios, such as popular warehouse systems and autonomous aircraft towing vehicles. With the development of reinforcement learning (RL) especially in the context of asynchronous advantage actor-critic (A3C), pathfinding is undergoing a revolution in terms of efficient parallel learning. Similar to other artificial intelligence-based applications, A3C-based pathfinding is also threatened by the adversarial attack. In this paper, we are the first to study the adversarial attack to A3C, that can unexpectedly wake up longtime retraining mechanism until successful pathfinding. We also discover an attack example generation to launch the attack based on gradient band, in which only one baffle of extremely few unit lengths can successfully perform the attack. Experiments with detailed analysis are conducted to show a high attack success rate of 95% with an average baffle length of 2.95. We also discuss defense suggestions leveraging the insights from our analysis.

Socialnet becomes an important component in real life, drawing a lot of study issues of security and safety. Recently, for the features of graph structure in socialnet, adversarial attacks on node classification are exposed, and automatic attack methods such as fast gradient attack (FGA) and NETTACK are developed for per-node attacks, which can be utilized for multinode attacks in a sequential way. However, due to the overlook of perturbation influence between different per-node attacks, the above sequential method does not guarantee a global attack success rate for all target nodes, under a fixed budget of perturbation. In this paper, we propose a parallel adversarial attack framework on node classification. We redesign new loss function and objective function for nonconstraint and constraint perturbations, respectively. Through constructing intersection and supplement mechanisms of perturbations, we then integrate node filtering-based P-FGA and P-NETTACK in a unified framework, finally realizing parallel adversarial attacks. Experiments on politician socialnet dataset Polblogs with detailed analysis are conducted to show the effectiveness of our approach.

Connected vehicles (CVs) are getting increasing attention in intelligent transportation systems (ITSs). In terms of the stringent security and anonymity issues, a novel anonymous and decentralized payment platform is demanded. Bitcoin is regarded as a potential solution. But the existing Bitcoin and its various improvements lack a beforehand anti-fraud Bitcoin deposit scheme, which is necessary to prevent potential fraud risks from Bitcoin to CVs. This paper aims to build a novel anti-fraud deposit scheme as a seamless bridge between CV networks and the Bitcoin payment platform. We propose a Bitcoin-to-Connected-Vehicle deposit scheme (Bit2CV) with an outsourcing endorsement in order to build an anti-fraud deposit transaction (dtr). Firstly, Bit2CV leverages a special Bitcoin-opcode-OP_RETURN based method to record the dtr, the CV's request, and the endorsement together on Blockchain ledger. This method can achieve security features, including non-repudiation, privacy-friendly endorsement, future audit, and anti-fraud. Meanwhile, Bit2CV is fully decentralized without any involvement of centralized authorities and fully compatible with the current Bitcoin network. We conduct the security analysis and also simulations for performance evaluation. In a typical scenario of our simulation, Bit2CV total time cost is less than 477.03 ms, and the size of the endorsement is 1,732 bytes, which is much less and smaller than transaction confirmation time and the average block size, respectively. These designs and results demonstrate that Bit2CV is feasible and practical.

In blockchain networks, reaching effective supervision while maintaining anonymity to the public has been an ongoing challenge. In existing solutions, certification authorities need to record all pairs of identities and pseudonyms, which is demanding and costly. This article proposed an account guarantee scheme to realize feasible supervision for existing anonymous blockchain networks with lower storage costs. Users are able to guarantee anonymous accounts with account guarantee key pairs generated from certificated polynomial functions, which inherently maintains one-to-n mapping certifications. Single or limited account guarantee key pairs do not leak privacy. Victims are able to request TCs to screen a cheater or disclose a cheater with enough fraud transactions by themselves. Detailed security and privacy analysis showed that the account guarantee scheme preserves user privacy and realizes feasible supervision. Experimental results demonstrated that the account guarantee scheme is efficient and practical.

IP-based geolocation is essential for various location-aware Internet applications, such as online advertisement, content delivery, and online fraud prevention. Achieving accurate geolocation enormously relies on the number of high-quality (i.e., the fine-grained and stable over time) landmarks. However, the previous efforts of garnering landmarks have been impeded by the limited visible landmarks on the Internet and manual time cost. In this paper, we leverage the availability of numerous online webcams used to monitor physical surroundings as a rich source of promising high-quality landmarks for serving IP-based geolocation. In particular, we present a new framework called GeoCAM, which is designed to automatically generate qualified landmarks from online webcams, providing an IP-based geolocation service with high accuracy and wide coverage. GeoCAM periodically monitors websites hosting live webcams and uses the natural language processing technique to extract the IP addresses and latitude/longitude of webcams for generating landmarks at a large-scale. Given latency and topology constraints among webcam landmarks, GeoCAM uses the maximum likelihood estimation to approximately pinpoint the geolocation of a target host. We develop a prototype of GeoCAM and conduct real-world experiments for validating its efficacy. Our results show that GeoCam can detect 282,902 live webcams hosted in webpages with 94.2% precision and 90.4% recall, and then generate 16,863 stable and fine-grained landmarks, which are two orders of magnitude more than the landmarks used in prior works. To demonstrate the superiority of using large-scale webcams as landmarks, we implement four different geolocation algorithms and compare their performance between webcam landmarks and open-source landmarks. The evaluation results show that all the algorithms can significantly improve geolocation accuracy by using webcam landmarks.

Adversarial example (AE), an input that is modified slightly to cause a machine learning system to produce erroneous outputs, has seen significant studies recently. Unfortunately, the fine data perturbation of AE ignores to keep potential data correlations between feature pairs. Thus, such AE will be easily filtered by configuring data correlations as basic filtering rules. In this paper, avoiding not to be filtered as well as causing false classification, an advanced robust AE generation attack is proposed. We first define four basic data correlations called strict linear constraint, approximate linear constraint, addition boundary constraint and zero multiplication constraint. Then, based on embedding multiple data correlations into one constraint matrix from the Pearson analysis, our approach can enable a Hadamard product of the constraint matrix and the sign of gradient matrix to craft perturbations, keeping consistent data correlations. Experimental results on intrusion detection system (IDS) indicate: 1) Nearly all AEs from original IFGSM are invalid by filtering according to basic data correlations; 2) In our method, AEs against a targeted DNN-based classifier can achieve an attack success rate of 99%, with transfer attack ability of 94% average success rate to attack other different mainstream classifiers.

With connected vehicle(CV) technology, the next-generation transportation system is stepping into its implementation phase via the deployment of Intelligent Traffic Signal System (I-SIG). Since the congestion attack was firstly discovered in USDOT (U.S. Department of Transportation) sponsored I-SIG, deployed in three cities including New York, such realistic threat opens a new security issue. In this work, from machine learning perspective, we perform a systematic feature analysis on congestion attack and its variations from last vehicle of different traffic flow pattern. We first adopt the Tree-regularized Gated Recurrent Unit (TGRU) to make explainable congestion attack prediction, in which 32-dimension features are defined to character a 8-phase intersection traffic. We then develop corresponding software-level security reinforcements suggestions, which can be further expanded as an important work. In massive experiments based on real-world intersection settings, we eventually distill 384 samples of congestion attacks to train a TGRU-based attack prediction model, and achieve an average 80% precision. We further discussed possible reinforcement defense methods according to our prediction model.

Advancements in distributed ledger technologies are rapidly driving the rise of decentralized crowdsourcing systems on top of open smart contract platforms like Ethereum. While decentralized blockchain-based crowdsourcing provides numerous benefits compared to centralized solutions, current implementations of decentralized crowdsourcing suffer from fundamental scalability limitations by requiring all participants to pay a small transaction fee every time they interact with the blockchain. This increases the cost of using decentralized crowdsourcing solutions, resulting in a total payment that could be even higher than the price charged by centralized crowdsourcing platforms. This paper proposes a novel suite of protocols called NF-Crowd that resolves the scalability issue by reducing the lower bound of the total cost of a decentralized crowdsourcing project to O(1). NF-Crowd is a highly reliable solution for scaling decentralized crowdsourcing. We prove that as long as participants of a project powered by NF-Crowd are rational, the O(1) lower bound of cost could be reached regardless of the scale of the crowd. We also demonstrate that as long as at least one participant of a project powered by NF-Crowd is honest, the project cannot be aborted and the results are guaranteed to be correct. We design NF-Crowd protocols for a representative type of project named crowdsourcing contest with open community review (CC-OCR). We implement the protocols over the Ethereum official test network. Our results demonstrate that NF-Crowd protocols can reduce the cost of running a CC-OCR project to less than $2 regardless of the scale of the crowd, providing a significant cost benefit in adopting decentralized crowdsourcing solutions.

Abstract Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models. However, the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion. Previously, the seq2seq model has been explored to augment system-call sequences. Following this work, we propose a gated convolutional neural network (GCNN) model to thoroughly extract the potential information of augmented sequences. Also, in order to enhance the model’s robustness, we adopt adversarial training to reduce the impact of adversarial examples on the model. Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm. The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance. Furthermore, GCNN with adversarial training can enhance robustness significantly.

In answer aggregation of crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. Existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted. In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort. Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.

With the rapid development of wireless communication technology and intelligent mobile devices, unmanned aerial vehicle (UAV) cluster is becoming increasingly popular in both civilian and military applications. Recently, a swarm intelligence-based UAV cluster study, aiming to enable efficient and autonomous collaboration, has drawn lots of interest. However, new security problems may be introduced with such swarm intelligence. In this work, we perform the first detailed security analysis to a kind of flocking-based UAV cluster with 5 policies, an upgrade version of the well-known Boids model. Targeting a realistic threat in a source-to-destination flying task, we design a data spoofing strategy and further perform complete vulnerability analysis. We reveal that such design and implementation are highly vulnerable. After breaking through the authentication of ad hoc on-demand distance vector (AODV) routing protocol by rushing attack, an attacker can masquerade as the first-arrival UAV within a specific scope of destination and generate data spoofing of arrival status to the following UAVs, so as to interfere with their normal flying paths of destination arrival and cause unexpected arrival delays amid urgent tasks. Experiments with detailed analysis from the 5-UAV cluster to the 10-UAV cluster are conducted to show specific feature composition-based attack effect and corresponding average delay. We also discuss promising defense suggestions leveraging the insights from our analysis.

Frequent itemset mining, which is the essential operation in association rule mining, is one of the most widely used data mining techniques on massive datasets nowadays. With the dramatic increase on the scale of datasets collected and stored with cloud services in recent years, it is promising to carry this computation-intensive mining process in the cloud. Amount of work also transferred the approximate mining computation into the exact computation, where such methods not only improve the accuracy also aim to enhance the efficiency. However, while mining data stored on public clouds, it inevitably introduces privacy concerns on sensitive datasets. In this paper, we propose a new framework for enforcing privacy in frequent itemset mining, where data are both collected and mined in an encrypted form in a public cloud service. We specifically design three secure frequent itemset mining protocols on top of this framework. Our first protocol achieves more efficient mining performance while our second protocol provides a stronger privacy guarantee. In order to further optimize the performance of the second protocol, we leverage a minor trade-off of privacy to get our third protocol. Finally, we evaluate the performance of our protocols with extensive experiments, and the results demonstrate that our protocols obviously outperform previous solutions in performance with the same security level.

While much effort has been made to detect and measure the privacy leakage caused by the advertising (ad) libraries integrated in mobile applications, analytics libraries, which are also widely used in mobile apps have not been systematically studied for their privacy risks. Different from ad libraries, the main function of analytics libraries is to collect users' in-app actions. Hence, by design analytics libraries are more likely to leak users' private information. In this work, we study what information is collected by the analytics libraries integrated in popular Android apps. We design and implement a framework called “Alde”. Given an app, Alde employs both static analysis and dynamic analysis to detect the users' in-app actions collected by analytics libraries. We also study what private information can be leaked by the apps that use the same analytics library. Moreover, we analyze apps' privacy policies to see whether app developers have notified the users that their in-app action data is collected by analytics libraries. Finally, we select eight widely used analytics libraries to study and apply our method to 300 popular apps downloaded from both Chinese app markets and Google play. Our experimental results show that some apps indeed leak users' personal information through analytics libraries even though their genuine purposes of using analytics services are legal. To mitigate such threats, we have developed an app named “ALManager” that leverages the Xposed framework to manage analytics libraries in other apps.

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.

Machine learning (ML) algorithms provide better performance than traditional algorithms in various applications. However, some unknown flaws in ML classifiers make them sensitive to adversarial examples generated by adding small but fooled purposeful distortions to natural examples. This paper aims to investigate the transferability of adversarial examples generated on a sparse and structured dataset and the ability of adversarial training in resisting adversarial examples. The results demonstrate that adversarial examples generated by DNN can fool a set of ML classifiers such as decision tree, random forest, SVM, CNN and RNN. Also, adversarial training can improve the robustness of DNN in terms of resisting attacks.

Summary Jaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP‐JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.

Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.

The recent proliferation of Internet of Things (IoT) device coupled with the demand for an inexpensive way of transmitting data has been the primary factors behind the popularity of the short-range acoustic communication. It enables a seamless transfer of digital information via soundwaves, using device's loudspeaker and microphone only and the whole interaction takes place without the need for network connection. Due to the limited computational power of the IoT device, the short-range acoustic communication system has adopted the friendly jamming technology to save energy. However, the security of the friendly jamming technology in mobile applications has not been thoroughly studied. When propagating sound in public, the soundwaves are subject to eavesdropping by nature. In particular, the friendly jamming technology is vulnerable to the separation attacks which can separate data signals from mixed signals. In this paper, we propose PriWhisper+-a secure acoustic short-range communication system between IoT devices. We analyze the security of PriWhisper+ via information theory and propose physical security enhancement mechanisms for acoustic communication by combining device mobility with secret sharing scheme. We then design a secure data communication scheme that transmits data in acoustic signals. This scheme can be applied in many security-sensitive situations, such as device pairing, contactless payments, and privacy data sharing. At last, we evaluate the performance of our proposed PriWhisper+ by extensive experiments on Android smartphones. The results of the experiment show that the PriWhisper+ can protect the data confidentiality within thirty centimeters of communication range.

Assisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.

Nowadays, the threats of Internet are enormous and increasing, however, the classification of huge alert messages generated in this environment is relatively monotonous. It affects the accuracy of the network situation assessment, and also brings inconvenience to the security managers to deal with the emergency. In order to deal with potential network threats effectively and provide more effective data to improve the network situation awareness. There is almost no alerts filtering in the existing network situation assessment and decision making process. Or existing job processing has a large alerts filter granularity and there are many redundant alert data. It is essential to build a hierarchical filtering method to prevent the threats. In this paper, it establishes a method for data monitoring, which can filter systematically from the original data to get the grade of threats and be stored for using again. Firstly, it filters multi- source alerts based on the vulnerable resources, open ports of host devices and services. Then calculate the performance changes of the host devices at the time of the threat occurring, and filter the data using the difference of performance entropy again. At last, it sorts the changes of the performance value at the time of threat occurring. The alerts and performance data are collected in the real network environment, and the comparative experimental analysis shows that the threat filtering method can effectively filter the threat alerts.

Cyber-attacks become more and more complex, but the network situation assessment based on log analysis cannot meet the security requirements because of the low quality of logs and alerts. This paper addresses the lack of consideration of security attributes of hosts and attacks in network. What's more, the most common attacks, identity and effectiveness of Distributed Denial of Service (DDoS) are hard to be proved in risk assessment based on alerts and flow matching. The multi-dimension threat situation assessment method based on network security attributes is proposed in this paper. Firstly, it gives an adaptive Common Vulnerability Scoring System (CVSS) calculation, which considers asset value as environment metric. Secondly, it collects deterioration rate of properties by sensors in hosts and network, that aims at assessing the time and level of DDoS attacks. Thirdly, it adopts the distribution of asset value in security attributes considering the features of attacks and network, which aims at assessing and showing the whole situation. Experiments demonstrate that the results show the primary threat and security requirement of network. By comparison and analytic study, the method reflects more in security requirement and security risk situation than traditional methods based on alert and flow analyzing.

Ring Learning With Errors (RLWE)-based key exchange is one of the most efficient and secure primitive for post-quantum cryptography. One common approach to achieve key exchange over RLWE is error reconciliation. Recently, an efficient attack against reconciliation-based RLWE key exchange protocols with reused keys was proposed. This attack can recover a long-term private key if a key pair is reused. We also know that in the real world, key reuse is commonly adopted in applications like the Transport Layer Security (TLS) protocol to improve performance. Directly motivated by this attack, we construct a new randomized RLWE-based key exchange protocol against this attack. Our lightweight approach incorporates an additional ephemeral public error term into key exchange materials, so that this attack no longer works. With the same attack, we practically show that the signal value of our protocol is indistinguishable from uniform random, therefore, this attack no longer works. We explain how the attack fails, present 200-bit classic and 80-bit quantum secure parameter choice, efficient implementations, comparisons and discussion. Benchmark shows our protocol is truly efficient and even faster than related vulnerable protocols.

Heterogeneity prevails not only among physical machines but also among workloads in real IaaS Cloud data centers (CDCs). The heterogeneity makes performance modeling of large and complex IaaS CDCs even more challenging. This paper considers the scenario where the number of virtual CPUs requested by each customer job may be different. We propose a hierarchical stochastic modeling approach applicable to IaaS CDC performance analysis under such a heterogeneous workload. Numerical results obtained from the proposed analytic model are verified through discrete-event simulations under various system parameter settings.

With wide use of cloud computing and storage services, sensitive information is increasingly centralized into the cloud to reduce the management costs, which raises concerns about data privacy. Encryption is a promising way to maintain the confidentiality of outsourced sensitive data, but it makes effective data utilization to be a very challenging task. In this paper, we focus on the problem of private matching over outsourced encrypted datasets in identity-based cryptosystem that can simplify the certificate management. To solve this problem, we propose an Identity-Based Private Matching scheme (IBPM), which realizes fine-grained authorization that enables the privileged cloud server to perform private matching operations without leaking any private data. We present the rigorous security proof under the Decisional Linear Assumption and Decisional Bilinear Diffie-Hellman Assumption. Furthermore, through the analysis of the asymptotic complexity and the experimental evaluation, we verify that the cost of our IBPM scheme is linear to the size of the dataset and it is more efficient than the existing work of Zheng and Xu [30]. Finally, we apply our IBPM scheme to build two efficient schemes, including identity-based fuzzy private matching as well as identity-based multi-keyword fuzzy search.

The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users' identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users' identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.

New advances in securing cyberspace and curbing crowdturfing

Cloud data center (CDC) network virtualization is being regarded as a promising technology to provide performance guarantee for cloud computing applications. One critical issue in CDC network virtualization technology is virtual data center (VDC) embedding, which deals with the CDC physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VDC embedding problem is NP-hard, even in the offline case. Node heterogeneity and CDC network scale bring challenges to the VDC embedding. This paper aims to embed a VDC in a robust and green way. We propose two effective, computation-efficient and energy-efficient embedding algorithms. Extensive simulations under various network scales and topologies are carried out to compare the proposed algorithms with the existing VDC embedding algorithms in terms of the VDC acceptance ratio, the long-term revenue of the cloud service provider (CSP), the CDC’s long-term energy consumption in light-load CDCs, and in terms of CSP's long-term revenue in heavy-load CDCs.

Summary A grouping‐proof protocol is to generate a proof that a group of tags have been scanned simultaneously in the range of a reader. Security and efficiency become the core issues when designing a grouping‐proof protocol. In this paper, we first describe the deployment model, adversary model, and security requirements of a grouping‐proof protocol. Then, we propose an offline reading order‐independent grouping‐proof protocol. To demonstrate security of our scheme, we present an ideal grouping‐proof functionality in the universally composable framework. Subsequently, we prove that the proposed protocol realizes the defined ideal functionality. Compared with the existing grouping‐proof protocols, our scheme satisfies the privacy and security requirements of a grouping‐proof protocol including tag/reader anonymity, tag/reader location privacy, mutual authorized access, anti‐active attack, anti‐replay attack, concurrent security, and so on. Meanwhile, our scheme has desirable performance. It conforms to the computational constraints of low‐cost passive radio frequency identification tags, which makes it suitable for large‐scale application. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In order to resolve the problems of node multiple keys management and protected storage in mobile ad hoc networks, a new identity‐based node key management scheme combined with next generation protected storage of portable trusted platform module was presented for mobile ad hoc networks. In the scheme, the combined public key based on elliptic‐curve cryptography was adopted to generate multiple keys, which were protected and managed by the next generation protected storage of portable trusted platform module. We show that our scheme is semantically secure under the elliptic‐curve DDH assumption. Also, performance evaluation of proposed scheme in a real‐world ad hoc network indicates the feasibility in practice. Copyright © 2015 John Wiley & Sons, Ltd.

This paper develops a novel approximate analytical model to evaluate the performance of active virtual machines in IaaS clouds using an M/G/m/m+K queue. The proposed model, combined with the transform-based analytical approach, enables the computation of the probability distribution of the number of jobs in the system and subsequently a set of performance measures, including the mean number of jobs in the system, the mean response time, the probability of immediate service, and the blocking probability. Compared to the existing Markov models of cloud data centers, our approach can reflect the system behavior more accurately even when the service-time distribution has a large coefficient of variation (> 1.5) in a medium-sized IaaS cloud. Numerical results obtained from the proposed analytical model are verified through extensive simulations under various system parameter settings, and compared with the results from existing models.

Smartphone users are facing serious threat of privacy leakage. This privacy leakage is caused not only by malicious applications (apps), but also by the most popular apps in one's pocket. In this poster, we present our study on the issues of information leakage caused by the most widely used apps in Chinese app markets. Our goal is to find what information is exposed by each popular app, and then to focus on the following three questions in order to explore the influence of this kind of information leakage: (1) to what extent can the information leaked by an app be used to characterize the user's behaviors? (2) to what extent can the information leaked by a number of apps in the same smartphone be used to characterize the user's behaviors' and (3) whether the leaked information from a number of smartphones can be integrated to predict the social behaviors' Preliminary experimental results on the top 50 popular apps in Chinese app markets show the serious situation of this kind of information leakage.

Today's Android-powered smartphones are equipped with various embedded sensors, such as the motion sensors, the environmental sensors and the position sensors. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can access data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users' privacy. In this work, we are motivated to provide an up-to-date overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in all the apps collected from a complete app market. To fulfill this goal, We develop a tool called “SDFDroid” to identify the sensors' types and to generate the sensor data propagation paths in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation paths. We apply our method on AppChina, a widely used Chinese Android app market. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage in the third-party libraries. Our results show the accelerometer sensor is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are some third-party libraries register all the types of sensors recklessly. These results show the need for better regulating the sensor usage in Android apps.

Summary Infrastructure as a Service cloud provides elasticity and scalable virtual machines (VMs) as computing service to multiple tenants, but the tenants lose the full control of their data. Measuring the integrity of critical files of the VMs and providing the integrity attestation to the tenants on the basis of TCG trusted computing techniques is an effective way to alleviate their anxiety. This paper considers how to measure the integrity of the processes run in guest VMs and files opened in guest VMs. We propose an out‐of‐the‐box integrity measurement approach to measure the integrity of critical files through system call (syscall) interception without any modification of the guest VMs. Out‐of‐the‐box integrity measurement approach can not only measure the integrity of all files that have been considered by existing approaches but also measure the integrity of the system configuration files, program loaders, and script interpreters, which affect the system behaviors and integrity. The ability of supporting both system and manual measurement policies makes our approach flexible. We implement this approach in Xen hypervisor with little modification of the existing syscall interception method, and this approach can be ported to other virtualization platform easily. Copyright © 2014 John Wiley & Sons, Ltd.

Efficiently processing massive data is a big issue in high‐speed network intrusion detection, as network traffic has become increasingly large and complex. In this work, instead of constructing a large number of features from massive network traffic, the authors aim to select the most important features and use them to detect intrusions in a fast and effective manner. The authors first employed several techniques, that is, information gain (IG), wrapper with Bayesian networks (BN) and Decision trees (C4.5), to select important subsets of features for network intrusion detection based on KDD'99 data. The authors then validate the feature selection schemes in a real network test bed to detect distributed denial‐of‐service attacks. The feature selection schemes are extensively evaluated based on the two data sets. The empirical results demonstrate that with only the most important 10 features selected from all the original 41 features, the attack detection accuracy almost remains the same or even becomes better based on both BN and C4.5 classifiers. Constructing fewer features can also improve the efficiency of network intrusion detection.

Android has been a major target of malicious applications (malapps). How to detect and keep the malapps out of the app markets is an ongoing challenge. One of the central design points of Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, it imparts a significant responsibility to the app developers with regard to accurately specifying the requested permissions and to the users with regard to fully understanding the risk of granting certain combinations of permissions. Android permissions requested by an app depict the app's behavioral patterns. In order to help understanding Android permissions, in this paper, we explore the permission-induced risk in Android apps on three levels in a systematic manner. First, we thoroughly analyze the risk of an individual permission and the risk of a group of collaborative permissions. We employ three feature ranking methods, namely, mutual information, correlation coefficient, and T-test to rank Android individual permissions with respect to their risk. We then use sequential forward selection as well as principal component analysis to identify risky permission subsets. Second, we evaluate the usefulness of risky permissions for malapp detection with support vector machine, decision trees, as well as random forest. Third, we in depth analyze the detection results and discuss the feasibility as well as the limitations of malapp detection based on permission requests. We evaluate our methods on a very large official app set consisting of 310 926 benign apps and 4868 real-world malapps and on a third-party app sets. The empirical results show that our malapp detectors built on risky permissions give satisfied performance (a detection rate as 94.62% with a false positive rate as 0.6%), catch the malapps' essential patterns on violating permission access regulations, and are universally applicable to unknown malapps (detection rate as 74.03%).

The virtual network embedding (VNE) problem has been receiving significant research interest lately. A realistic scenario might entail embedding multiple VN requests (MVNE) that arrive simultaneously (batch arrivals). The existing MVNE approaches either did not consider the coordination among multiple VNR embeddings or embedded all the arriving VNRs simultaneously without considering the substrate network state. This paper considers the MVNE problem in the scenarios where the available physical resources may not be sufficient to satisfy the physical resource demands of all the VNRs in the batch. We formulate the problem as mixed integer programs, which not only provide optimal MVN embedding but also improve the network service provider's long-term revenue. Simulation results validate the revenue gains of the proposed approach over the existing exact and heuristic MVNE approaches.

This paper aims to defeat TCG TOCTOU attacks occurring in trusted para-virtualized machines (TPVM). We propose a robust and efficient response mechanism (RERM). Compared to the existing response mechanisms, RERM is more effective in defeating the TPVM TCG TOCTOU attacks without incurring CPU overhead during the normal system execution. We verify the security ability of RERM via both model checking based formal verification techniques and experiments.

The virtual network embedding (VNE) problem deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in the offline case. The genetic algorithm (GA) is an excellent approach to solving complex problems in optimization with difficult constraints. This paper explores applying GA to handle the VNE problem. We propose two GA-based VNE algorithms and evaluate them by comparing with the existing state-of-the-art VNE algorithms, including PSO-based VNE approaches. Extensive simulation results validate the capability of the proposed GA-based VNE algorithms in terms of the InP long-term revenue and the VN embedding cost.

Multi-path routing has long been studied as an important routing strategy in networks. Many multi-path routing protocols schedule traffic among multiple paths in order to distribute traffic load. However, existing multi-path routing protocols with traffic assignment require that all nodes in the network follow the protocol, which may not always be a valid assumption when the network consists of selfish nodes. In this paper, we propose an optimal, strategy-proof scheme for multi-path traffic assignment (OSMA) in non-cooperative networks. When OSMA is used, behaving honestly is to the best interest of each selfish node regardless of any other nodes¿ behavior. Furthermore, our scheme is guaranteed to compute the lowest cost traffic assignment with the existence of these selfish nodes. Our evaluations verify that our scheme is optimal and strategy-proof, and demonstrate that the scheme has very low communication and computation overhead.

The current TCG architecture suffers from the Time-of-Check-To-Time-of-Use (TOCTOU) attacks in commodity PC operating systems (OS), in which kernel rootkits can get unrestricted access to OS resources. VMM-based approaches running at a privilege level higher than that of virtual machine (VM) kernel can effectively detect dynamic or static data attacks occurring in VMs. This paper proposes a lightweight response mechanism (LWRM) for TCG TOCTOU attacks occurring in VMs. LWRM has the following features: (1) compared to the existing response mechanism, LWRM is more effective in defeating the TCG TOCTOU attacks; (2) LWRM imposes less overhead on the system during normal execution; (3) LWRM is transparent to the kernel rootkits; and (4) LWRM can work in the scenarios with more than one run-time trusted virtual machine. We describe the design idea and the implementation by using the Xen virtual machine monitor (VMM) and the virtual TPM facility shipped with the Xen.

Interrupt coalescing (IC) technique has been used in general-purpose operating systems to mitigate receive livelock (RL) problem in gigabit Ethernet network hosts. Schemes for dynamically tuning the interrupt coalescing behavior of a communication interface based on traffic load or system state have been proposed. However, all the existing IC schemes are designed using heuristics. In this paper we present an analytical model for the IC technique and carry out a detailed study of existing IC schemes in terms of their performance characteristics including system goodput, CPU consumption and latency. We validate our analysis through measurement-based experiments.

Remote attestation is an important attribute in trusted computing. One of the purpose of remote attestation is to attest the remote platform is trusty but not revealing the actual identity of the platform. Direct anonymous attestation (DAA) is a kind of scheme which is adopted by Trusted Computing Group in the specification 1.2 to hide the privacy of the platform. But DAA involves various of zero-knowledge proofs and is not efficient to implement. To guarantee the trustworthiness and privacy, we propose a remote anonymous attestation protocol based on ring signature in this paper. We also show that our protocol is secure under the RSA assumption in random oracle model. Furthermore, the attestation protocol does not need the third party and extra zero-knowledge proof, which makes it very efficient in realization.

Multipath routing has long been studied as an important routing strategy in networks. Many multipath routing protocols schedule traffic among multiple paths in order to distribute traffic load. However, existing multipath routing protocols with traffic assignment require that all nodes in the network follow the protocol, which may not always be a valid assumption when the network consists of selfish nodes. In this paper, we propose a traffic assignment scheme to deal with the selfish behavior, which is proved to be strategy-proof. Under our scheme, behaving cooperatively is to the best interest of every node. Extensive evaluations are carried out to show that our scheme has good performance.