Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across contexts. While usually associated with friendliness or playfulness, it is observed that emojis may trigger toxic content generation in LLMs. Motivated by such a observation, we aim to investigate: (1) whether emojis can clearly enhance the toxicity generation in LLMs and (2) how to interpret this phenomenon.* We begin with a comprehensive exploration of emoji-triggered LLM toxicity generation by automating the construction of prompts with emojis to subtly express toxic intent. Experiments across 5 mainstream languages on 7 famous LLMs along with jailbreak tasks demonstrate that prompts with emojis could easily induce toxicity generation. To understand this phenomenon, we conduct model-level interpretations spanning semantic cognition, sequence generation and tokenization, suggesting that emojis can act as a heterogeneous semantic channel to bypass the safety mechanisms. To pursue deeper insights, we further probe the pre-training corpus and uncover potential correlation between the emoji-related data polution with the toxicity generation behaviors.

Relational databases (RDBs) are widely used by corporations and governments to store multiple related tables. Their relational schemas pose unique challenges to synthetic data generation for privacy-preserving data sharing, e.g., for collaborative analytical and data mining tasks, as well as software testing at various scales. Relational schemas typically include a set of primary and foreign key constraints to specify the intra-and inter-table entity relations, which also imply crucial intra-and inter-table data correlations in the RDBs. Existing synthetic RDB generation approaches often focus on the relatively simple and basic parent-child relations, failing to address the ubiquitous real-world complexities in relational schemas in key constraints like composite keys, intra-table correlations like sequential correlation, and inter-table data correlations like indirectly connected tables. In this paper, we introduce incremental relational generator (IRG), a modular framework designed to handle these real-world challenges. In IRG, each table is generated by learning context from a depth-first traversal of relational connections to capture indirect inter-table relationships and constructs different parts of a table through several classical generative and predictive modules to preserve complex key constraints and data correlations. Compared to 3 prior art algorithms across 10 real-world RDB datasets, IRG successfully handles the relational schemas and captures critical data relationships for all datasets while prior works are incapable of. The generated synthetic data also demonstrates better fidelity and utility than prior works, implying its higher potential as a replacement for the basis of analytical tasks and data mining applications. Code is available at: https://github.com/li-jiayu-ljy/irg.

The proliferation of Internet of Things (IoT) devices necessitates robust authentication mechanisms to ensure security and privacy in resource-constrained environments. Existing authentication protocols face limitations, including vulnerability to physical attacks, high computational overhead, and scalability challenges. This paper presents a novel IRS-aided authentication mechanism for IoT devices that leverages Intelligent Reflecting Surfaces (IRS) to enhance location-based authentication through controllable signal enhancement. Our approach employs IRS to amplify Received Signal Strength (RSS) variations in response to device mobility, enabling precise location change detection while integrating pseudo-identity verification and RSS filtering to mitigate Denial of Service (DoS) attacks. Comprehensive robustness analysis demonstrates exceptional performance under realistic deployment constraints: simulations with Rician channel models show 99.82%−99.99% authentication performance across commercial IRS implementations (1-bit to 3-bit phase shifters), with 4.6 dB signal enhancement over direct paths and minimal system overhead (< 0.03%). The mechanism achieves high suitability for infrastructure-dense deployments, including smart buildings and industrial IoT, while providing comprehensive protection against major attack vectors with lower computational complexity compared to existing protocols.

This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method leverages DRL methodology not only to execute precise and detrimental impacts on targeted infrastructure but also to evade detection by contemporary AI-driven defence systems. By developing and implementing tailored policies, the attackers ensure their hostile actions blend seamlessly with normal operational patterns, circumventing integrated security measures. Our research reveals the robustness of this attack strategy, shedding light on the potential for DRL models to be manipulated for adversarial purposes. Our research has been validated through testing and analysis in an industry-level setup. For reproducibility and further study, all related materials, including datasets and documentation, are publicly accessible.

In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, often original data cannot be shared due to privacy concerns and regulations. A potential solution is to release a synthetic dataset with a similar distribution to the private dataset. Nevertheless, in some scenarios, the attributes required to train an AI model are distributed among different parties, and the parties cannot share the local data for synthetic data construction due to privacy regulations. In PETS 2024, we recently introduced the first Vertical Federated Learning-based Generative Adversarial Network (VFLGAN) for publishing vertically partitioned static data. However, VFLGAN cannot effectively handle time-series data, which contains both temporal and attribute dimensions. In this article, we proposed VFLGAN-TS, which combines the ideas of attribute discriminator and vertical federated learning to generate synthetic time-series data in the vertically partitioned scenario. The performance of VFLGAN-TS is close to that of its centralized counterpart, which represents the upper limit for VFLGAN-TS. To further protect privacy, we apply a Gaussian mechanism to make VFLGAN-TS satisfy an (ε ,δ)-differential privacy. Besides, we develop an enhanced privacy auditing scheme to evaluate the potential privacy breach through the framework of VFLGAN-TS and synthetic datasets.

The proliferation of Unmanned Aerial Vehicle (UAV) networks and their numerous benefits in critical scenarios, UAV become crucial for Internet of Drones (IoD) operations. However, due to their communication methods, such as the micro-air-vehicle communication (MAVlink) protocol, wireless connections, and potentially insecure Internet channels, UAV networks are highly vulnerable to potentially lethal attacks. To overcome such issues, public key cryptographic techniques relying on integer factorization problem (IFP) and discrete logarithm problems (DLP) have been used form decades. However, with the significant advancements in quantum computing and adaptation of Shor's algorithm such cryptographic techniques based on IFP and DLP become insecure today and vulnerable to quantum attacks, which demand new ways of thinking about security. In this paper, we propose a quantum-secure access control protocol for UAV-based IoD applications, and its primary focus is on preserving user anonymity. A comprehensive security analysis validates the accuracy, security, and resilience against various active and passive attacks in both classical and quantum scenarios. A thorough formal security verification using the Scyther automated software validation tool to showcases the robustness of the proposed scheme. Furthermore, a real-time testbed experiment on Raspberry Pi 4 devices to assess the computational overhead of various cryptographic primitives demonstrates its practicality. Lastly, a detailed comparative performance evaluation, including authentication accuracy, performance under unknown attacks with existing related schemes illustrates its scalability and efficiency in real-world applications.

Global Positioning System (GPS) technology is widely used in personal and industrial applications to acquire precise timing and positional information. However, its open-standard signals are vulnerable to spoofing attacks, which can cause serious damage if undetected. Employing detection methods is crucial in critical applications. Machine Learning (ML) methods have been successfully applied for spoofing detection, typically performing detection on individual samples. This work proposes a framework that takes a multivariate time-series window as input, enabling the neural network model to extract meaningful temporal information from the sample window for improved detection performance. We train a Convolutional Variational Autoencoder model using spoof-free samples under a representation learning framework. The detector's performance is evaluated using the publicly available TEXBAT dataset and simulated datasets. Our results show that the proposed detector achieves a True Positive Rate (TPR) above 99% for a low False Positive Rate (FPR) of 2% in both static and dynamic attack scenarios. Additionally, for the sophisticated attack scenario (DS-7) in the TEXBAT dataset, our detector achieved a TPR of 89% for an FPR of 3%, highlighting its robustness against different types of spoofing attacks.

The rapid evolution of communication networks to 5G/6G has introduced significant challenges, particularly in ensuring data security, low latency, high throughput, efficient energy consumption, dynamic access to multiple connection types, and managing the influx of connected devices. Quantum communication, leveraging entangled states like the perfect W-state, offers a promising solution with its high degree of entanglement, secure information transmission, and resilience against decoherence. However, practical applications and experimental validations remain limited, especially regarding the integration with Local Operations and Classical Communication (LOCC) protocols. Additionally, optimizing perfect W-state-based communication protocols for the splitting and sharing of quantum information has been scarcely explored. This article presents a Quantum Information Sharing and Splitting (QISS) protocol that integrates perfect W-states with LOCC to enhance 5G and 6G communication. Using the Eagle r3 processor based on superconducting qubits, our experiments demonstrated a fidelity of 0.82 ± 0.02 for the perfect W-state circuit and 0.55 ± 0.03 for the integrated W-state + LOCC in the QISS communication prototype. These findings, quantified through Quantum State Tomography, significantly improve communication security, network densification, and effectiveness. Furthermore, our research addresses existing gaps in quantum communication implementation, paving the way for scalable quantum networks and advanced encryption methods. This work marks a substantial step towards secure and efficient data transmission in next-generation communication systems.

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a$\sim 10^{-4}$second inference latency on a laptop CPU.

Recent studies show that diffusion models (DMs) are vulnerable to backdoor attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a gray box and eyeglasses) that contain evident patterns, rendering remarkable attack effects yet easy detection upon human inspection and defensive algorithms. While it is possible to improve stealthiness by reducing the strength of the backdoor, doing so can significantly compromise its generality and effectiveness. In this paper, we propose UIBDiffusion, the universal imperceptible backdoor attack for diffusion models, which allows us to achieve superior attack and generation performance while evading state-of-the-art defenses. We propose a novel trigger generation approach based on universal adversarial perturbations (UAPs) and reveal that such perturbations, which are initially devised for fooling pre-trained discriminative models, can be adapted as potent imperceptible backdoor triggers for DMs. We evaluate UIBDiffusion on multiple types of DMs with different kinds of samplers across various datasets and targets. Experimental results demonstrate that UIBDiffusion brings three advantages: 1) Universality, the imperceptible trigger is universal (i.e., image and model agnostic) where a single trigger is effective to any images and all diffusion models with different samplers; 2) Utility, it achieves comparable generation quality (e.g., FID) and even better attack success rate (i.e., ASR) at low poison rates compared to the prior works; and 3) Undetectability, UIBDiffusion is plausible to human perception and can bypass Elijah and TERD, the SOTA defenses against backdoors for DMs. Code is available at https://github.com/TheLaoLab/UIBDiffusion.

The industrial Internet of things (IIoT) relies on programmable logic controllers (PLCs) for critical operations, therefore making them prime targets for cyber-attacks especially when the program is manipulated with malevolent intent. Current attestation methods either need ongoing monitoring of the PLC program during runtime which results in substantial computational burden, or rely on physical models that are challenging to accurately develop and maintain with precision. This paper introduces a novel and efficient attestation method exclusively developed for IIoT settings, which effectively combines efficiency and security, particularly in legacy PLCs that may not have sufficient computing capabilities. Contrary to continuous attestation, this approach conducts periodic attestation at intervals and selectively validates different parts of the PLC program randomly against the legitimate PLC program. Implementing this focused strategy decreases the computational load while ensuring a strong probability of detecting unauthorized modifications. Experimental verification demonstrates that our approach achieves a total verification time of 11.93 ms (i.e. improving execution time by up to 17.67% over existing techniques), and maintains detection accuracy above 90%, thereby offering superior efficiency and security for both contemporary and older Programmable Logic Controllers in industrial environments.

Communication in the green Internet of Vehicles (IoV) demands significant energy, encompassing both communication and computation costs, along with fuel and electricity for vehicle operation. The rise of quantum computing threatens the security of existing IoV frameworks, particularly those relying on conventional public-key cryptosystems (PKC) like integer factorization and elliptic curve cryptography, which are vulnerable to quantum attacks. This paper proposes a lightweight, postquantum security protocol for electric vehicles (EVs) in IoV, aimed at reducing computation and communication costs while enhancing energy efficiency. We conduct a comprehensive security analysis and compare our protocol with existing solutions, demonstrating its superior security, scalability, and practical effectiveness. Network simulations using NS3 further validate the robustness and efficiency of the proposed scheme for green IoV applications.

WiFi-based sensing technology has gained significant attention for its ability to enable pervasive human activity recognition (HAR) in indoor spaces. One challenge is that current WiFi HAR systems often experience performance degradation in unseen scenarios (e.g., new environments, people, and weather conditions). Some research has attempted to address this issue by extracting scenario-invariant features using deep learning (DL). However, discarding scenario-specific features brings about insufficient representation of task-related information, resulting in limited model adaptability. In this paper, we present GenFi, a robust WiFi HAR system that enhances model generalization by leveraging both scenario-invariant and scenario-specific features. To achieve this, GenFi first disentangles the raw input into these two types of features through adversarial learning and correlation analysis. Subsequently, GenFi uses meta-learning to self-optimize the fusion of these two features, leading to a generalized cross-scenario WiFi HAR system. Compared to state-of-the-art approaches, GenFi achieves the best trade-off between high performance and low complexity in diverse unseen scenarios, making it a promising solution for real-world deployment.

The rise of 5G technology has highlighted the critical role of Thick Mobile Virtual Network Operators (MVNOs) in providing customized mobile services. However, security and privacy challenges specific to Thick MVNOs remain inadequately addressed. In this paper, we present PGUS (Pretty Good User Security) for Thick MVNOs. Our proposed PGUS framework introduces a new cryptographic primitive called the Sanitizable Blind Signature (SBS), along with a novel Authentication and Key Agreement protocol named PGUS-AKA. Additionally, we have developed a seamless handover protocol, PGUS-HO, which is designed to secure all communication within a Thick MVNO environment. Furthermore, we conduct a thorough formal security analysis within the Universal Composability (UC) framework to address key threats, providing a strong solution for securing next-generation mobile networks. We also provide the evaluations on a 5G testbed which demonstrate the effectiveness of PGUS.

Non-Terrestrial Networks (NTN) are capable of providing wide communication coverage. Autonomous vehicles can leverage NTN to access emergency services when terrestrial networks are affected by disasters. However, there are security challenges associated with communication over wireless links of NTN. Therefore, authentication of vehicles requesting emergency service is essential. This paper highlights the benefits of NTN in emergency scenarios and proposes a secure framework for autonomous vehicles to access emergency services in NTN. In the proposed framework, Unmanned Aerial Vehicle (UAV), which is a part of NTN, acts as a relay node. As emergency services are time-critical operations, we have designed the proposed protocol leveraging lightweight cryptographic operations, thus keeping computation costs to a minimum. We have provided formal security analysis using the Burrows-Abadi-Needham (BAN) logic and informal security analysis to demonstrate the security of the proposed protocol. Performance analysis shows that the computation and communication costs of the proposed protocol are less than those of other existing schemes for NTN authentication.

As the world rapidly embraces quantum technologies, the need for robust quantum security protocols becomes increasingly paramount. Quantum key distribution (QKD) has been at the forefront of secure key exchange, but establishing a root of trust remains unaddressed. This research article presents a pioneering approach for global system for mobile communications (GSM) that bridges the gap between QKD and device identity verification. Our approach utilizes single-qubit states and amplitude encoding, integrating the BB84 protocol to securely share secret keys between entities. We implement two-factor authentication (2FA) to further protect against attacks and unauthorized access. Unlike entangled state-based schemes that require quantum memory and face practical implementation challenges, our single-qubit approach avoids these issues, making it feasible with current technology. Central to our approach is the verification of the subscriber identity module (SIM) card holder’s authenticity using the quantum prover authentication protocol (QuPAP) at the mobile authentication Center. This quantum cryptography-based process enhances GSM communication security and can be integrated into existing networks with minimal modifications. Our proposed smartphone, equipped with dual SIM capabilities—one conventional (cSIM) and one quantum (QuSIM)—ensures compatibility with both current and future networks, allowing the benefits of quantum security without requiring a complete system overhaul. By integrating quantum security into classical GSM protocols, our scheme not only enhances security but also addresses crucial aspects of device identity authentication, attestation, and trust establishment. The security and performance analysis of the QuPAP prototype demonstrates a quantum leap in mobile security, fostering a future of trust, privacy, and resilience in the ever-evolving landscape of communication technologies.

Amidst the large-scale deployment of Internet of Things (IoT) networks worldwide, studies have highlighted critical security concerns many of which stem from firmware-related issues. IoT swarms have become more prevalent in industries, smart homes, and agricultural applications and malicious activity on one node can propagate to other network sections. While several remote attestation (RA) techniques have been proposed in the literature, they are limited by their latency, availability, complexity, hardware assumptions, and uncertain access to firmware copies under intellectual property (IP) rights. To address these problems, we present Swarm-Net, a novel swarm attestation technique that uses graph neural networks (GNNs) to exploit the inherent, interconnected, graph-like structure of IoT networks and the runtime information stored in the static random access memory (SRAM). We also present the first datasets on SRAM-based swarm attestation encompassing different types of firmware and edge relationships. In addition, a secure swarm attestation protocol is proposed to ensure authentication, availability, and attestation. Swarm-Net is computationally lightweight and does not require a copy of the firmware. It achieves a 99.96% attestation rate on authentic firmware, 100% detection rate (DR) on anomalous firmware, and 99% DR on propagated anomalies, at a communication overhead and inference latency of ~1 s and$\sim 10^{-5}$s (on a laptop CPU), respectively. In addition to the collected datasets, Swarm-Net’s effectiveness is evaluated on simulated trace replay, random trace perturbation, and dropped attestation responses, showing robustness against such threats. Lastly, we compare Swarm-Net with past works and present a security analysis.

The widespread application of AI-generated content (AIGC) services has driven demand for efficient computational resources, making effective task scheduling and computation offloading in edge computing (EC) environments a critical research topic. However, the high computational requirements and low latency demands of AIGC services, combined with the limitations of EC, present challenges for existing offloading methods, such as unstable decision making in dynamic task environments and resource overloading. Here, we propose a decentralized AIGC task offloading architecture within an IoT-assisted EC network to optimize the quality of AIGC services. In this architecture, we define a multiobjective joint optimization problem for AIGC task offloading, aiming to simultaneously optimize key performance metrics, such as task latency, energy efficiency, and load balancing. To address this problem, we introduce an improved proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm, named TOPPO. By incorporating a policy update step size constraint and a clipping mechanism, TOPPO significantly enhances the stability of the training process and reduces fluctuations during policy updates. Additionally, the algorithm integrates an LSTM model to improve its ability to handle temporal dependencies. Through continuous interaction between the model and the environment, the offloading strategy is iteratively updated to ensure that diverse AIGC tasks are efficiently executed on IoT devices or edge servers. Extensive simulations and performance evaluations demonstrate that the proposed method achieves significant improvements in task latency, energy consumption, and load management during AIGC task processing.

Data privacy is a critical concern in the digital age. This problem has compounded with the evolution and increased adoption of machine learning (ML), which has necessitated balancing the security of sensitive information with model utility. Traditional data privacy techniques, such as differential privacy and anonymization, focus on protecting data at rest and in transit but often fail to maintain high utility for machine learning models due to their impact on data accuracy. In this article, we explore the use of synthetic data as a privacy-preserving method that can effectively balance data privacy and utility. Synthetic data is generated to replicate the statistical properties of the original dataset while obscuring identifying details, offering enhanced privacy guarantees. We evaluate the performance of synthetic data against differentially private and anonymized data in terms of prediction accuracy across various settings—different learning rates, network architectures, and datasets from various domains. Our findings demonstrate that synthetic data maintains higher utility (prediction accuracy) than differentially private and anonymized data. The study underscores the potential of synthetic data as a robust privacy-enhancing technology (PET) capable of preserving both privacy and data utility in machine learning environments.

Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. The availability of smart meter data has led to the demand for sensor-based load forecasting. Conventional ML allows training a single global model using data from multiple smart meters requiring data transfer to a central server, raising concerns for network requirements, privacy, and security. To alleviate this issue, we propose a split learning-based framework for load forecasting. We split a deep neural network model into two parts, one for each Grid Station (GS) responsible for an entire neighbourhood's smart meters and the other for the Service Provider (SP). Instead of sharing their data, client smart meters use their respective GSs' model split for forward passes and only share their activations with the GS. Under this framework, each GS is responsible for training a personalized model split for their respective neighbourhoods, whereas the SP can train a single global or personalized model for each GS. Experiments show that the proposed models match or exceed a centrally trained model's performance and generalize well. Privacy is analyzed by assessing information leakage between data and shared activations of the GS model split.

Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile transport problem, in particular, contributes significantly to the expenditure of human resources in major companies. Robot-based delivery systems have emerged as a potential solution for last-mile delivery to address this challenge. However, robotic delivery systems still face security and privacy issues, like impersonation, replay, man-in-the-middle attacks (MITM), unlinkability, and identity theft.In this context, we propose a privacy-preserving multi-factor authentication scheme specifically designed for robot delivery systems. Additionally, AI-assisted robotic delivery systems are susceptible to machine learning-based attacks (e.g. FGSM, PGD, etc.). We introduce the first transformer-based audio-visual fusion defender to tackle this issue, which effectively provides resilience against adversarial samples. Furthermore, we provide a rigorous formal analysis of the proposed protocol and also analyse the protocol security using a popular symbolic proof tool called ProVerif and Scyther. Finally, we present a real-world implementation of the proposed robotic system with the computation cost and energy consumption analysis. Code and pre-trained models are available at: https://github.com/YYangNUS/TIFS RobotMFA.

Multi-signature protocols allow a group of signers to collectively generate a single signature for a shared message. In the context of a decentralized blockchain, multi-signature schemes play a pivotal role in reducing the signature size. Recently, several multi-signature methods have emerged in the literature, some operating in discrete-log settings and others in lattice settings. However, many of the existing lattice-based multi-signature schemes incur high computation costs and online round complexity. Traditional public key-based multi-signature schemes are susceptible to quantum threats, and they are computationally intensive as well. A lattice-based multi-signature can provide robust security, which often falls short in terms of efficiency when it comes to round complexity. In this article, we aim to introduce a single-round lattice-based multi-signature scheme specifically designed for decentralized public blockchains. What sets the proposed scheme apart is its ability to function without the need for trapdoor commitments or sample pre-images, which are common features in existing lattice-based signature methods. Furthermore, we explore some potential applications in a generic Internet of Things (IoT) environment and their integration of the proposed scheme with the blockchain technology. The security of the proposed scheme is based on lattice-hard problems, like Ring-SIS (Shortest Integer Solution) and Ring-LWE (Learning with Errors).

The Internet of Things (IoT) has revolutionized connectivity by enabling a large number of devices to autonomously exchange real-time data over the Internet. However, IoT devices used in public spaces are vulnerable to physical and cloning attacks. To address this issue, researchers have introduced the concept of physical-unclonable functions (PUFs) to enhance security in IoT applications. While PUF-based security solutions typically rely on static challenge-response behavior, many practical applications require dynamic or reconfigurable PUFs. For instance, PUF-based key storage may require updating or revoking secrets, and protection against modeling attacks, where an attacker can derive a PUF model from a set of challenge-response pairs (CRPs) using learning capabilities. In this paper, we introduce LR-OPUF, a reconfigurable one-time PUF, and propose a lightweight and privacy-preserving authentication scheme based on this LR-OPUF foundation. One notable feature of our authentication scheme is that it enables a device to prove its legitimacy to a semi-honest verifier without disclosing the CRPs. Through security and performance analyses, we demonstrate that our approach not only ensures vital security aspects but also exhibits high computational efficiency.

Traditional Network Intrusion Detection Systems (NIDS) often generate large volumes of alerts with redundancies and false positives, incapable of correlating detected attack actions. This adds difficulty for security analysts to construct a comprehensive understanding of multi-step attacks. To address these limitations, we present a novel MITRE-based Multi-step Attack Scenario Construction (M2ASK) algorithm that enhances cyber threat intelligence (CTI) by integrating MITRE ATT&CK tactic and technique mapping, facilitating the interpretation of multi-step attacks and informing response strategies. Our approach processes alert data from NIDSs, transforming it into a network communication graph. Graph-based correlation techniques are employed, combined with MITRE ATT&CK and Cyber Kill Chain stage profiling to construct comprehensive network attack scenarios. Our key contributions include: (1) the development of a Cyber Kill Chain based model for constructing attack scenarios; (2) the alert correlation approach based on MITRE ATT&CK tagging of attack actions.

Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a pervasive proliferation of connected devices, ranging from high-power computers to low-power sensors. Yet, the complex IoT architecture poses numerous vulnerabilities, demanding robust security measures. Existing firmware attestation techniques often encounter obstacles due to proprietary constraints, necessitating access to the device’s authentic firmware. To address this challenge, this paper proposes a novel software-based attestation framework that utilizes RAM traces from IoT devices for remote verification. By employing deep learning models trained in a representation learning paradigm, our framework empowers the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, our approach achieves an impressive 100% detection rate for critical attacks on IoT devices with a false positive rate of 10−3. Remarkably, our framework preserves device availability and maintains low authentication latency, highlighting its efficacy and practicality for securing IoT ecosystems.

Privacy concerns have become increasingly prominent as machine learning (ML) models are adopted in an increasing number of sectors. The potential of unintended or malicious exposure of sensitive data, especially in E-Health solutions, has increased as these models are shared and deployed more broadly. In order to highlight the important problem of property inference attacks, which can result in privacy and data confidentiality breaches, this study focuses on inferring global characteristics of the underlying datasets used to train the ML models. Building upon the intriguing work by Ateniese et al. on property inference attacks on ML models, we present a novel property inference attack using Variational Auto-Encoders (VAEs). VAEs offer a strong answer to the difficult problem of inferring dataset attributes because of their reputation for being successful in modeling complex data distributions and producing synthetic data samples. Experiments on three healthcare and the US census datasets show that the proposed attack can effectively reveal underlying patterns in the training dataset with up to 94.29% accuracy. A comparison with the popular meta-classifier based property inference attacks shows that the proposed attack not only has better success rate, but can do so with half training data and a smaller number of shadow models.

As electric vehicles (EVs) gain widespread acceptance for sustainable transportation, robust testing and validation for related technologies are becoming difficult due to challenges in acquiring real-world data due to limited availability, high costs, and privacy concerns. To address this issue, this paper introduces the 3D-time-series Generative Adversarial Network (3DTS GAN) to generate high-resolution, multivariate synthetic driving data for EV systems. Integrating Auto-encoder and GAN structures, the proposed method addresses the shortcomings of existing data generation methods, offering a more comprehensive representation of driving data. Evaluation results show that this method is able to generate synthetic data that is similar to original driving data with higher similarity scores than those attained using existing methods. Moreover, a functional check is done to demonstrate that there is no significant difference between using the original driving data and the synthetic data to perform further tasks such as energy consumption prediction.

Vehicular platooning offers several advantages and plays an important role in the future of mobility. However, this technology is vulnerable to several attacks. Since vehicles can freely join and leave a platoon, it is important to ensure that only legitimate vehicles are admitted into a platoon. In this paper, we propose a mutual authentication protocol to securely admit vehicles into a platoon. The proposed protocol is built on the concepts of Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Each vehicle creates its DID which helps to achieve vehicle identity privacy. The vehicles must register with a Trusted Authority (TA) to join platoons. The TA issues a VC to the registered vehicle. The platoon leader and the vehicle joining the platoon verify each other’s VC before the vehicle joins the platoon. The security analysis demonstrates that the proposed protocol ensures secure platoon admission and preserves the privacy of vehicles. We also provide a proof of concept implementation of the blockchain network for the proposed scheme using Ethereum. We use the online Integrated Development Environment (IDE) Remix to compile and run the smart contract written in Solidity code for the proof of concept implementation. A performance analysis of the proposed protocol shows that its computation cost is less than that of other existing schemes for platoon admission.

The accelerating deployment of unmanned aerial vehicles (UAVs) is accompanied by escalating security concerns, especially with regard to communication protocols. Traditional cryptographic mechanisms, while functional, fall short in computational efficiency and low-latency requirements that are critical for UAV networks. Addressing these challenges, this article introduces a novel hardware-secured authentication and attestation mechanism tailored for UAV-to-UAV data exchange. The mechanism is designed to scale efficiently with UAV swarms and withstand rigorous post-deployment verifications. Our research contributions are multifaceted, comprising: 1) a feasibility and security validation of the proposed protocol via Mao-Boyd logic, providing a robust theoretical foundation; 2) empirical results that confirm the protocol’s superior performance over contemporary solutions in both speed and security; and 3) a comprehensive security and performance analysis to ensure the protocol’s resilience against potential vulnerabilities. Thus, this article presents a balanced and effective approach to secure UAV communications, satisfying both computational and security demands.

With the proliferation of 4G and 5G mobile networks in smart cities, the adoption of Internet of Things (IoT) devices has surged, emphasizing the critical need for robust security measures. Existing firmware attestation techniques often require high computational budget or access to the device’s authentic firmware, posing challenges due to resource and proprietary constraints. To counter these two fundamental challenges, this article introduces a novel software-based attestation framework utilizing RAM traces from IoT devices for remote verification. In the proposed framework, the need for an authentic firmware copy is eliminated, and the most computationally intensive task is assigned to the gateway node of the IoT ecosystem. This approach yields a robust and highly accurate device attestation strategy, while imposing minimal computational demands on the verification device itself. Employing deep learning models trained in a representation learning paradigm, our framework enables the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, under eight different applications, our approach achieves a remarkable 100% accuracy in detecting critical attacks on IoT devices with a false positive rate of$10^{-3}$. Notably, our framework preserves device availability and maintains low authentication latency, underscoring its efficacy and practicality for securing IoT ecosystems.

The number of the Internet of Things (IoT) devices is growing at a rapid pace. Although the IoT has and continues to enable many new and exciting applications, recent studies show that cyberattacks on these Internet-connected low-powered devices are constantly increasing. One crucial security aspect for the IoT is device attestation, i.e., verifying the integrity of an IoT device’s firmware/software. Existing techniques for IoT device attestation are either vulnerable to physical attacks or rely on unrealistic assumptions in terms of hardware requirements. To solve these issues, this article presents Soteria, a novel quantum-powered remote attestation technique using quantum physical unclonable functions (QPUFs) which offer enhanced security by leveraging the unique properties of quantum mechanics. Soteria also exploits quantum superposition to attest multiple memory locations in parallel, and thus, protecting it from roving malware. A security analysis of Soteria shows that it is secure against various types of attacks, while a performance analysis shows that it achieves its desired security properties while maintaining low-computational complexity.

The Internet of Things (IoT)-enabled ride sharing is one of the most transforming and innovative technologies in the transportation industry. It has myriads of advantages, but with increasing demands there are security concerns as well. Traditionally, cryptographic methods are used to address the security and privacy concerns in a ride sharing system. Unfortunately, due to the emergence of quantum algorithms, these cryptographic protocols may not remain secure. Hence, there is a necessity for privacy-preserving ride sharing protocols which can resist various attacks against quantum computers. In the domain of privacy-preserving ride sharing, a threshold private set intersection (TPSI) can be adopted as a viable solution because it enables the users to determine the intersection of private data sets if the set intersection cardinality is greater than or equal to a threshold value. Although TPSI can help to alleviate privacy concerns, none of the existing TPSI is quantum secure. Furthermore, the existing TPSI faces the issue of long-term security. In contrast to classical and post quantum cryptography, quantum cryptography (QC) provides a more robust solution, where QC is based on the postulates of quantum physics (e.g., Heisenberg uncertainty principle, no cloning theorem, etc.) and it can handle the prevailing issues of quantum threat and long-term security. Herein, we propose the first QC-based TPSI protocol which has a direct application in privacy-preserving ride sharing. Due to the use of QC, our IoT-enabled ride sharing scheme remains quantum secure and achieves long-term security as well.

Flexibility and reconfigurability make field-programmable gate arrays (FPGAs) ideal for IoT applications because they enable efficient customization and optimization of hardware acceleration tasks in diverse IoT applications. Malicious hardware trojans pose a significant security threat, capable of compromising the integrity of reconfigurable devices such as FPGAs. The majority of current attestation schemes either demonstrate complexity and demand significant resources or lack versatility. To solve this issue, this article proposes a novel lightweight runtime attestation approach to detect hardware trojans or malicious modifications in a hardware design. The proposed technique can verify the integrity of both the hardware design’s finite state machine (FSM) and its datapath. Attesting the FSM ensures the accuracy of state transitions and control behavior while verifying the datapath validates the data processing operations. When combined, these provide a comprehensive validation of the overall hardware functionality. A trusted verifier initiates challenges by stipulating a starting state and an input sequence to the prover. The prover then executes these challenges and reports the observed responses, i.e., state transitions, control outputs, status outputs, and timing metrics. Anomalies between the expected and observed behaviors serve as indicators of potential trojan interventions. The proposed method’s efficacy is substantiated through simulation and implementation on a Zynq-7000 SoC, showcasing its efficiency in terms of resource utilization overhead. Collectively, this study advances the capabilities of remote attestation while bolstering the security of reconfigurable platforms.

Due to their flexibility and agility, unmanned aerial vehicles (UAVs) offer a promising approach to cluster planning within wireless sensor networks (WSNs). However, the limited battery capacity of a single UAV limits its application in many situations, such as searching in wild areas. In this article, we propose a computational scheme of cooperative path planning for heterogeneous UAVs based on Voronoi diagrams and intelligent swarm optimization algorithm. In this article: 1) Voronoi diagrams are used to model the field environment according to the radar sensor position; 2) an improved$K$-medoids algorithm based on the maximum empty circle property of the Voronoi diagram (Vor-$K$-medoids) is proposed to complete the reconnaissance UAVs (RUAVs) domain cooperative search; and 3) a hyperbolic tangent heuristic function intelligent optimization algorithm is proposed to calculate the minimum risk path for the attack UAV (AUAV) according to the characteristics of the attack mission. The simulation results show that the proposed scheme integrates the properties of the Voronoi diagram, clustering algorithm, and path planning algorithm commendably. Compared with the traditional ant colony optimization (ACO), under the same number of iterations, the probability of obtaining the optimal track is improved by 14%, and the running time is shortened by 50.87%.The proposed scheme offers a practical and cost-effective approach for efficiently searching areas within large-scale radar sensors in real-world scenarios.

In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private dataset. Nevertheless, in some scenarios, it has been found that the attributes needed to train an AI model belong to different parties, and they cannot share the raw data for synthetic data publication due to privacy regulations. In PETS 2023, Xue et al. [29] proposed the first generative adversary network-based model, VertiGAN, for vertically partitioned data publication. However, after thoroughly investigating, we found that VertiGAN is less effective in preserving the correlation among the attributes of different parties. This article proposes a Vertical Federated Learning-based Generative Adversarial Network, VFLGAN, for vertically partitioned data publication to address the above issues. Our experimental results show that compared with VertiGAN, VFLGAN significantly improves the quality of synthetic data. Taking the MNIST dataset as an example, the quality of the synthetic dataset generated by VFLGAN is 3.2 times better than that generated by VertiGAN w.r.t. the Frechet Distance. We also designed a more efficient and effective Gaussian mechanism for the proposed VFLGAN to provide the synthetic dataset with a differential privacy guarantee. On the other hand, differential privacy only gives the upper bound of the worst-case privacy guarantee. This article also proposes a practical auditing scheme that applies membership inference attacks to estimate privacy leakage through the synthetic dataset.

Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF’s security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded ‘secure’ if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis.

Unmanned aerial vehicles (UAVs) have a great potential for assigning search and rescue operations in emergency scenarios. However, emergency scenarios are complex and unknown, regarding UAVs to reschedule to effectively adapt to the changing environment, and existing literature addressing this challenge is limited. To address this open problem, we consider a task rescheduling problem with uncertainties such as task insertion, edge computing node (ECN) destruction, and parameter fluctuation in UAV-assisted emergency networks. The goal is to minimize the fine-grained makespan, defined as the ratio of makespan to ECNs idle time, that simultaneously characterizes the optimization of rescheduling efficiency and ECNs utilization. To address the problem, we propose an asynchronous shuffled frog-leaping with feasible Jaya (ASFJ) algorithm. In ASFJ, an asynchronous shuffled frog-leaping method independently evolves memeplexes, thereby avoiding forced information coverage. Two feasible local search operators promote the search capability and feasibility of the algorithm. Finally, we verify the advantages of the ASFJ in terms of makespan, effectiveness, and fine-grained makespan. ASFJ can save 3.83ms makespan and outperform 11.2% fine-grain makespan in insertion rescheduling. The effectiveness of destruction rescheduling is improved by at least 16%.

Intelligent Transportation Systems (ITS) optimize road network capacity, monitor traffic flow, and enhance overall road safety by analyzing real-time trajectory data. However, the utilization of such data raises privacy concerns, enabling potential attackers to gain insights into users’ real-time activities and personal information. Furthermore, existing privacy preservation methods have multiple limitations, particularly in low-traffic density environments. To address these issues, this paper presents a novel approach for generating realistic trajectories that evade tracking. Existing trajectory generation mechanisms are coarse-grained and cannot adequately preserve the quality of location-based services while safeguarding individual privacy. To overcome this limitation, we first use differential privacy to determine a location near the actual destination and employ a path search algorithm to extract relevant road information. Subsequently, by leveraging our hybrid reinforcement learning model, we generate trajectories leading to this fictitious point. The comparison conducted on real-world maps with other trajectory generation methods reveals its superior ability to preserve spatio-temporal features. Finally, we propose two approaches that use the generated trajectories to protect privacy, ensuring both individual privacy protection and the utility of data.

Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks (e.g., blackhole, grayhole and flooding attacks). To determine the most effective configuration of the proposed construction, we conduct a number of experiments using several state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest (ARF), light gradient-boosting machine, and K-nearest neighbours) and concept drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our experimental results show that incremental ARF using ADWIN provides optimal performance when implemented with One-class support vector machine (SVM) anomaly-based detectors. Furthermore, our extensive evaluation results also show that the proposed scheme outperforms state-of-the-art bench-marking methods while providing a wider range of desirable features such as scalability and complexity.

Existing wireless infrastructure and networks are unable to meet the diverse Quality of Service (QoS) demands inherent in a wide range of consumer healthcare applications (CHAs). In this context, the adoption of fifth generation of wireless cellular technology (5G)/Beyond fifth-generation (B5G)-based network slicing technology has become pivotal for CHAs. It facilitates the creation of multiple virtual networks on a shared physical infrastructure by catering to distinct QoS requirements, where digital twins (DTs) are providing a virtual representation and management framework for healthcare smart devices, services, and applications within network slices. This allows different services and applications to coexist. However, network slicing has to address various security concerns, including securing slice access, enabling secure inter-slice communication, ensuring slice isolation within the shared physical network with DTs, and authenticating end users. To address these challenges, we propose a security mechanism that is specifically designed to safeguard network slice access and isolation in CHAs empowered by DTs, where only legitimate devices with corresponding digital twins and matching attributes are granted access. The proposed model incorporates the use of digital certificates for authenticating both slice access and devices by providing enhanced slice isolation to mitigate unauthorized access. Through a detailed comparative assessment, we demonstrate that the proposed scheme offers superior security and improved functionality attributes, while maintaining low communication costs as compared to those for other similar existing schemes. Furthermore, we validate the feasibility of our scheme through testbed simulations.

As Information Technology (IT) infrastructures have become increasingly complex to secure against accelerating cyber threats, current threat detection approaches have been largely silos in nature; security analysts in the environment are typically bombarded with large volume of security alerts that often cause severe fatigues and the possibility of judgement errors. This problem is further exacerbated by the number of false-positives that analysts may waste valuable time and resources pursuing. In this paper, we present how intuitive graph-based machine learning can be used to address the problem of alert fatigue and prioritize risky alerts to assist security analysts. The rationale and workflow of the proposed Graph Analysis (GA) algorithm is discussed in detail, with its effectiveness demonstrated by simulated experiments.

Federated Learning (FL)-based Intrusion Detection Systems (IDSs) have recently surfaced as viable privacy-preserving solution to decentralized grid zones. However, conventional synchronous FL methods face technical challenges including the lack of consideration of communication delays and straggler nodes. To level the playing field, we propose a novel power system misbehaviour detection framework that leverages semi-asynchronous federated learning and dynamic aggregation. Specifically, our framework introduces an adaptive learning rate mechanism in the semi-asynchronous FL setting, allowing for efficient model updates and mitigating the impact of stragglers on the training process. Experiments conducted on publicly available Mississippi State University and Oak Ridge National Laboratory Power System Attack (MSU-ORNL PSA) Dataset demonstrate that our adaptive learning semi-asynchronous FL framework achieves superior attack detection rate while safeguarding data confidentiality and minimizing the negative effects of practical world communication latency and straggler nodes. Furthermore, our proposed method shows a significant 40% improvement in training time compared to conventional synchronous FL methods, showcasing the effectiveness and efficiency of our recommended approach.

The reliable operation of phasor measurement units (PMU) in modern power grid monitoring system like wide-area measurement systems (WAMS) relies on accurate time synchronization, which is provided by the Global Positioning System (GPS). However, the open nature of civilian GPS signals makes PMUs vulnerable to time synchronization attacks (TSA), where attackers manipulate PMU time stamps by transmitting deceptive GPS signals near the PMUs. In this paper, we propose a framework for detecting TSA on PMUs using machine learning (ML) methods. We evaluate five ML algorithms, including Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boost, and Artificial Neural Network, and select seven complementary features that can be computed at the radio frequency (RF) and tracking stages of any commercial GPS receiver. Our detection protocol stands out from other similar ML-based methods in terms of speed, as it does not rely on waiting for the PVT solution. The Texas Spoofing Test Battery (TEXBAT) dataset is used to evaluate the proposed framework. We demonstrate that the ML models can effectively detect GPS spoofing with up to 99.9 % probability while maintaining less than 0.5 % false alarm and mis-detection probabilities. By providing early detection of GPS spoofing attacks on PMUs, the proposed framework has the potential to enhance the cybersecurity of WAMS.

In dynamic and unknown emergency networks, (unmanned aerial vehicle) UAV-assisted task scheduling is an important and efficient technique. However, the inevitable uncertainty makes the predetermined schedule decision unfeasible. In this paper, we investigate the effects of parameter fluctuation and unpredictable edge computing node (ECN) failure. We formulate the task rescheduling problem to effectively deal with uncertainty, and design an asynchronous shuffled frog-leaping with jaya (ASJ) algorithm to minimize the makespan, in which the jaya algorithm asynchronously evolves the memeplexes of the shuffled frog-leaping method. To verify the effectiveness of the proposed ASJ, experiments are conducted to compare it with two comparison algorithms under deterministic and uncertain scenarios. The results demonstrate the superiority of the ASJ in terms of makespan.

Drones are becoming popular in a variety of applications. One of them is to collect packages from sellers and deliver them to buyers who are connected through a market-place platform. However, drones are also vulnerable to cyber-attacks. Drone delivery service also brings up privacy concerns about the personal information of users of the marketplace. This paper addresses such security and privacy threats and proposes a privacy-preserving authentication protocol for drone delivery services. The proposed protocol is built on privacy-preserving Decentralized Identifiers (DIDs) and Verifiable cre-dentials (VCs). The use of DID helps marketplace users to preserve their privacy rights and enables them to request drone services in a privacy-preserving manner. At the same time, their legitimacy can be verified by the drones using VCs. The proposed protocol also incorporates an efficient dynamic revocation mechanism to remove the marketplace users from the service subscription if required.

This poster presents a novel approach that harnesses large-sized deep neural networks to craft lightweight variants, addressing constraints in storage, processing speed, and task execution time on heterogeneous edge devices. Knowledge distillation is employed to refine the training of lightweight deep neural networks, and a novel early termination technique is introduced to optimize resource utilization and expedite the training process. This approach yields satisfactory accuracy while accommodating diverse heterogeneous edge device constraints.

The keyless entry systems in cars enable users to lock or unlock cars remotely. One of the popular types of keyless entry systems used in cars is the Remote Keyless Entry (RKE) system. With the advent of quantum computers, quantum computing-enabled cyber-attacks are an imminent threat. The security offered by current cryptographic techniques is inadequate to protect systems such as RKE from such future quantum attacks. In this paper, we propose a quantum-safe authentication protocol leveraging Quantum Key Distribution (QKD) that authenticates a legitimate key fob before unlocking the car. We present a formal security proof and an informal analysis to show that the protocol is secure against several attacks. To the best of our knowledge, this is the first protocol that protects RKE systems from future quantum computing-enabled cyber-attacks, in addition to the existing replay and RollJam attacks.

Electric Vehicle (EV) adoption has been increasing in recent years due to multiple factors. Though EVs offer many advantages, the cyber security of EV networks is often overlooked. When individuals charge their EVs at charging stations, the communication between the EV and the other components of the charging system is through the Internet. It is crucial to understand the potential attacks that an attacker could launch and propose solutions to prevent such attacks to safeguard the EV networks. In this paper, we address message tampering attacks on EV networks and propose a mechanism to prevent them. Existing solutions for message tampering are not suitable for EV networks due to the high computation cost and latency requirements. In the proposed solution, EVs generate authentication parameters based on the charging requests they transmit. The authentication parameters are delivered to a central server together with the charging requests. This enables the server to verify the integrity of the received charging requests. Since the proposed mechanism does not include computationally expensive operations, it does not add significant cost. We present a formal security proof to show that the proposed mechanism provides protection from message tampering attacks and achieves several security properties in the EV charging framework. A performance analysis is also presented to show the computation cost of the proposed mechanism.

Internet of Things (IoT) is evolving as a ubiquitous technology to thrive human lives with minimal time and effort. The resource-constrained IoT devices operating in an ambient environment with minimal or no safeguards are highly susceptible to physical invasion. The existing protocols suffer from huge computing resources required for cryptographic primitives and bandwidth overhead of high message passing during authentication. In addition, few of them suffer from multiple executions of disparate protocols incurring huge latency. Effective use of lightweight primitives with adequate security also propels to rethink the design of the IoT protocol. In this work, we developed a lightweight authentication and key exchange protocol that aptly suits the resource-constrained environment. The proposed protocol leverages cryptographic XOR, hash function for secure communication, and physically unclonable function (PUF) for unique device-dependent identity generation and lightweight security solution to prevent physical attacks. This standalone protocol can perform device-to-device and device-to-server authentication without incurring additional communication and computation resources, eradicating the need for disparate protocols. Extensive security analysis against adversarial attacks and bad PUF-model-based attacks are formally verified. In addition, a Scyther verification tool is utilized for security validation. Performance analysis advocates the lightweight features of this protocol. A prototype implemented with Xilinx Spartan-3E FPGA and Raspberry Pi for a smart street light monitoring system endorses the proposed protocol’s acceptability and safeguards against different adversarial attacks.

Blockchain (BC) and artificial intelligence (AI) technologies have independent applications in multiple industries, including banking, finance, healthcare, construction, transportation, hospitality, manufacturing, and insurance, to name a few. Moreover, these two technologies can be integrated seamlessly, thanks to their complementary and mutually supportive features. AI algorithms can make the medical BC storage efficient by their processing algorithms, also playing the role of knowledgeable gatekeepers. BC can support AI models by providing secure, sizeable, traceable, diverse, and immutable healthcare data for the training purpose. The integration of BC and AI has multiple use cases in the healthcare industry ranging from disease prediction to pandemic management. Previously, researchers have reviewed the applications of each of these technologies in healthcare independently. Although the integration of BC and AI has been fruitful, to the best of our knowledge, there has been no work in the past reviewing the confluence of these two technologies in the healthcare sector. We have classified the works based on two different classification schemes: 1) application-based and 2) AI-training paradigm-based classification. We have also provided a compilation of tools used in the integrated systems of BC and AI for healthcare. We identified that the integration of BC and AI technologies had been applied in quite different areas of healthcare ranging from biomedical research to pandemic management. It is also noted that the supervised learning algorithms and federated learning paradigm for secure decentralized AI model training are often used in the integration. Our findings reveal that majority of the reviewed works use BC as a secure database for AI models. Furthermore, we also have pointed out the potential applications of these two technologies in healthcare.

Health insurance is crucial for each person, bearing in mind the increasing medical costs. COVID-19 has been an eye-opener as to how important it is to have health insurance. Medical emergencies can have a severe emotional and financial impact. Thus, a health insurance policy can help mitigate financial risks in unpredictable circumstances. However, the current insurance system is very expensive, as thousands of people pay the premiums, and very few take the claims. Furthermore, the claim settlement process is excruciatingly long and tiresome. In this article, we focus on establishing a rapid and cost-effective framework for the health insurance market, based on machine learning and blockchain technology. By developing a smart contract, blockchain may eliminate any third-party organizations and make the complete process safer, easier, and more efficient. The contract pays the claim based on the claimant’s documentation. We optimized the premiums using a regression model based on the net amount claimed during the current policy tenure and various other criteria. For anticipating risk, a random forest classifier is used, which aids in the risk-rated premium rebate computation for policyholders for their next term of insurance.

Data collaboration with cloud technologies is becoming more popular for personal use as well as business applications. Due to the increasing data protection regulations worldwide, different cryptographic techniques have been designed to enable secure data sharing for a user or a group of users. Although, these techniques have seen enterprise adoption, they fail to offer data visibility as data remains encrypted throughout the data sharing routine. This is why these techniques fail to offer a key features to data users, e.g., joining different datasets together and sharing it with all the users involved. This paper presents a blockchain based architecture for secure data collaboration in cloud using differentially private synthetic data and trusted execution environment (TEE). The proposed solution protects data confidentiality and integrity with TEEs, supports public-key infrastructure (PKI) with blockchain, and prevents privacy leakages with synthetic data. The results show that our synthetic data performs as good as real data and demonstrates how different users can securely aggregate their datasets and openly share among themselves.

Keyless entry systems in cars give users the flexibility of unlocking the car door without using physical keys. Remote Keyless Entry (RKE) system is one of the common types of keyless entry systems. RKE systems that use a fixed code to unlock the car are susceptible to replay attacks. RKE systems that use rolling codes instead of fixed codes are still vulnerable to RollJam attacks. To protect RKE systems from such attacks, we propose a lightweight and secure mutual authentication mechanism based on Physical Unclonable Functions (PUFs). We provide formal security analysis to show that the proposed mechanism is resilient against several common attacks. We also provide a comparison with other existing schemes which shows that the proposed mechanism is very efficient in terms of computation cost.

The use of unmanned aerial vehicles (UAVs) for diverse activities has increased rapidly in recent years. Nonetheless, if operational cyber security is not handled effectively, these technologies offer a significant hazard which can cause catastrophic harm. Therefore, it is important to identify the potential attacks that can be implemented by an adversary. Traditional methods for data integrity designed for the Internet are not suitable for UAV assisted vehicular or wireless sensor networks due to the high communication overhead and latency required. This paper proposes a lightweight data integrity technique called MaDe to address this problem. Every device, at regular intervals, generates an authentication parameter that depends on the packets transmitted. The authentication parameters are only delivered to a central server or the device where the integrity of the packets is verified. At the server, MaDe takes the final decision about an UAV using a generalized likelihood ratio test. MaDe can identify malicious UAVs effectively as demonstrated through our performance analysis. The results show that MaDe detects malicious UAVs with minimum communication overhead and latency.

Modern cars come with Keyless Entry Systems that can be either Remote Keyless Entry (RKE) systems or Passive Keyless Entry and Start (PKES) systems. In the initial versions of RKE implementation, fixed code was used by the key fob to unlock the car door. However, this method is vulnerable to replay attacks as an adversary may capture and replay the same code later to unlock the car. A rolling code system was introduced to protect RKE systems from such replay attacks. Studies have shown that even the rolling code system is vulnerable to certain attacks. In this work, we investigate the attacks possible on RKE systems and propose an efficient and effective authentication mechanism to defend RKE systems against such attacks with minimal changes to the existing RKE system. The proposed mechanism makes use of hashing and asymmetric cryptographic techniques for the secure transmission of signals from the key fob to the car that cannot be replayed. The security of the proposed mechanism is shown using informal security proof and simulation of the proposed solution is also provided.

The Internet of Vehicles (IoV) may enhance road safety, improve traffic flow, etc. However, Internet-connected intelligent vehicles (IVs) are vulnerable to cyber-attacks. One of the important challenges in IoV is thus, verifying data integrity with strict latency requirements. The conventional way of providing data integrity in the Internet cannot be applied to IoV due to excessive overhead and latency. Therefore, most commercially available IVs do not use any security mechanisms for delay-sensitive traffic. However, if a road side unit (RSU) has been compromised, it can tamper with the data sent or received by IVs. To solve this issue, this article presents a light-weight mechanism called DRiVe to establish data integrity for the IVs and detect malicious RSUs. The DRiVe is based on a probabilistic model to identify malicious RSUs using specially constructed authentication techniques. The authentication parameters are only sent when a vehicle leaves the coverage area of one RSU and enters that of another. DRiVe does not employ any computationally intensive cryptographic primitives. This significantly reduces the security overhead introduced by sending message authentication codes (MACs) with each packet. A security and performance analysis shows that DRiVe can not only identify malicious RSUs effectively but can do so without introducing any significant communication overhead or latency. The proposed scheme reduces the number of bits transmitted by approximately 7% and decreases the latency incurred by 7.5%. For the scenario where malicious vehicles are present, the proposed scheme achieves a probability of detection close to 99%.

The advent of 4G and 5G mobile networks has made the Internet of Things (IoT) devices an essential part of smart nation drives. Firmware integrity is crucial to the security of IoT systems. Most of the existing techniques for firmware attestation require a legitimate copy of an IoT device’s firmware. However, firmware is considered an intellectual property (IP) of the manufacturer and may not be available. To solve this issue, this article proposes a software-based attestation technique where remote verifiers use machine learning (ML) classifiers on an IoT device’s memory dump to verify the integrity of an IoT device’s internal state. The experimental results from an actual prototype show that the proposed technique not only successfully detects attacks with high accuracy but also results in about 96% lower latency as compared to existing techniques. All this is achieved with high availability, low computational complexity, and without requiring a legitimate copy of the device’s original firmware.

Concurrent advancements in machine learning (ML) and Internet of Things have allowed several interesting interdisciplinary applications, such as classification tasks based on data generated by smart devices for applications, such as security, resource allocation, activity and task classification. However, these applications can be vulnerable to attacks by adversarial examples. The first contribution of this article is the development of a white-box adversarial attack mechanism to generate adversarial examples for data obtained from smart meters installed in residential houses. For the second contribution, we present an analysis to demonstrate that the statistical properties of adversarial datapoints are indistinguishable from those of the true datapoints. The attack is developed specifically for deep-learning-based models used to perform appliance classification in smart home environments. The statistical indistinguishability of the adversarial datapoints from the true datapoints indicates that non ML-based solutions may not be able to tackle the challenge posed by adversarial examples. As the final contribution, we evaluate the effectiveness of defence mechanisms for white-box adversarial attacks on the proposed attack mechanism, and show that while they can reduce the potency of the attack, the original models still remain significantly affected by the adversarial attack. The effectiveness of the proposed techniques is demonstrated on two publicly available data sets: 1) United Kingdom-domestic appliance-level electricity smart meter data set and 2) the Personalized Retrofit Decision Support Tools For U.K. Homes Using Smart Home Technology data set.

In this article, we propose a low-delay routing-integrated MAC (LDRI-MAC) protocol for wireless sensor networks (WSNs). In the LDRI-MAC protocol, we first assign a parameter termed as grade to each node. The grade of a node represents the minimum hop distance between the node and the sink. Then, we partition each node’s possible senders into disjoint sets (DSs) of size$\alpha $. We allow all$\alpha $nodes of a DS for data transmission in a cycle if a node of the DS succeeds in channel contention in the cycle. Thus, we allow$\alpha -1$nodes for data transmission even when they failed in channel contention in the same cycle. In this way, we reduce the channel access competition and the energy consumption in control overhead, overhearing, and idle listening. For performance evaluation, we develop a discrete-time Markov-chain (DTMC) model and drive closed-form expressions for packet delivery ratio (PDR), event-reporting delay$(D)$, and average energy consumption of a sensor node per received data packet at the sink (AEC). We also develop an algorithm to determine the value of$\alpha $that minimizes$D$. The results show that LDRI-MAC provides higher PDR, lower$D$, and greater energy efficiency, than H-MAC and JRAM protocols, in a multihop WSN.

Edge computing (EC), is a technological game changer that has the ability to connect millions of sensors and provide services at the device end. The broad vision of EC integrates storage, processing, monitoring, and control of operations in the Edge of the network. Though EC provides end-to-end connectivity, speeds up operation, and reduces latency of data transfer, security is a major concern. The tremendous growth in the number of Edge Devices and the amount of sensitive information generated at the device and the cloud creates a broad surface of attack and therefore, the need to secure the static and mobile data is imperative. This article is a comprehensive survey that describes the security and privacy issues in various layers of the EC architecture that result from the networking of heterogeneous devices. Second, it discusses the wide range of machine learning and deep learning algorithms that are applied in EC use cases. Following this, this article broadly details the different types of attacks that the Edge network confronts, and the intrusion detection systems and the corresponding machine learning algorithms that overcome these security and privacy concerns. The details of machine learning and deep learning techniques for EC security are tabulated. Finally, the open issues in securing Edge networks and future research directions are provided.

The theft of Intellectual property (IP) is a serious security threat for all businesses that are involved in the creation of IP. In this article, we consider such attacks against IP for Network-on-Chip (NoC) that are commonly used as a popular on-chip scalable communication medium for Multiprocessor System-on-Chip. As a protection mechanism, we propose a timing channel fingerprinting method and show its effectiveness by implementing five different solutions using this method. We also provide a formal proof of security of the proposed method. We show that the proposed technique provides better security and requires much lower hardware overhead (64%–74% less) compared to an existing NoC IP security solution without affecting the normal packet latency or degrading the NoC performance.

The Internet of Things (IoT) is becoming a revolutionary paradigm, moving toward ubiquity in day-to-day life and used in several applications such as smart healthcare systems, industry 4.0, critical infrastructure, etc. As with any concept that relies on wireless communication, authentication is of paramount importance in regards to security considerations. Devices in many IoT applications are severely constrained in terms of computational resources and are thus unable to utilize many modern cryptographic methods for security purposes. Physically unclonable functions (PUFs) propose to solve this issue by allowing devices to generate unique and secure digital fingerprints at extremely low computational cost. However, PUFs are vulnerable to machine learning based modeling attacks that can mathematically clone the PUFs in order to impersonate them. To address these requirements, this article introduces a new lightweight and practical anonymous authentication protocol for IoT that is resilient against machine learning attacks on PUFs.

Vehicular ad-hoc network (VANET) security has been an active area of research over the past decade. However, with the increasing adoption of the Internet of Things (IoT) in VANETs, the number of connected vehicles is set to grow exponentially over the next few years, which translates to a higher number of communication interfaces and a greater possibility of cybersecurity attacks. Along with these cybersecurity attacks, the instances of compromised vehicles sending faulty information about their positions and speeds also increase exponentially. Thus, there is a need to augment the existing security schemes with anomaly detection schemes which can differentiate normal vehicle data from malicious and faulty data. Since, the number of anomaly types can be many, deep neural networks would work best in this scenario. In this paper, we propose a deep neural network-based vehicle anomaly detection scheme. We use a sequence reconstruction approach to differentiate normal vehicle data from anomalous data. Numerical results show that we can correctly detect data corresponding to several anomaly types.

Vehicular Ad-Hoc Networks (VANETs) are a significant part of Intelligent Transportation Systems (ITS), and they are used to enhance the road safety and improve the traffic efficiency through the communication between vehicles and roadside units. However, some malicious adversaries can use the periodically broadcast beacons in VANETs to track vehicles. To mitigate this threat and protect privacy, existing research primarily suggests the use of pseudonyms as variable identities for each vehicle, and has explored different pseudonym mix strategies. These mix strategies often introduce latent traffic accident risks by requiring the vehicles to change their driving behavior or stop broadcasting. In this paper, we present a flickering context-based mix strategy, which can reduce such hidden dangers and provide higher privacy level than traditional context-based strategy. Besides, we employ a passive global adversary to evaluate the proposed strategy and conduct simulations in both virtual maps and real city maps to measure the protection level of the proposed privacy scheme. Finally, the influences of different parameters in our new method are explored.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

Having a health insurance is important for everybody, bearing in mind the increasing medical costs. Medical emergencies can have a severe financial and emotional impact. However, the current insurance system is very expensive and the claim settlement process is excessively lengthy, making it tedious. This results in policyholders not being able to successfully make a claim with their insurance company. In this paper, we focus on developing a fast and cost-effective framework based on blockchain technology and machine learning for the health insurance industry. Blockchain is capable of removing all third-party organisations by forming a smart contract, making the entire process more smooth, secure, and efficient. The contract settles the claim on documents submitted by the claimant. A ridge regression model is used for computing the premiums optimally, based on the total amount claimed under the current policy tenure, along with several other factors. A random forest classifier is applied for predicting the risk that helps in the computation of risk-rated premium rebate.

One of the most important and critical requirements for the Internet of Vehicles (IoV) is security under strict latency. Typically, authentication protocols for vehicular ad hoc networks need to authenticate themselves frequently. This results in reduced application traffic and increased overhead. Moreover, the mobile nature of vehicles makes them a prime target for physical, side channel, and cloning attacks. To address these issues, this article presents an efficient protocol for authentication in the IoV. The proposed protocol uses physical unclonable functions to provide the desired security characteristics. To reduce the overhead of authentication and improve the throughput of application layer packets, the proposed protocol uses a three-layered infrastructure architecture for IoVs, i.e., roadside units (RSUs), RSU gateways, and trusted authority. A vehicle needs to authenticate only once when it enters the area of an RSU gateway which may engulf multiple RSUs. A performance analysis of the protocol shows that the proposed strategy significantly reduces the number of authentication packets and MAC/PHY overhead while the security analysis demonstrates its robustness against various types of attacks.

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

Smart grids rely on communication networks to connect the physical devices and the control and computation technologies. The transmission of sensitive data over the network induces the possibility of leakage of private and sensitive information about various entities and components in the grid. To address this issue, this article proposes a privacy-preserving framework to enhance the privacy of smart grids integrated with software-defined networks. The proposed framework uses two privacy metrics (mutual information and differential privacy) and formulates a privacy-preserving distributed optimization algorithm with the objective of minimizing the network cost. We view the distributed optimization algorithm as an n-player, noncooperative game and provide distributed techniques to solve the optimization problem efficiently. We prove that our algorithm converges to the Nash equilibrium of the game while preserving the data privacy. We validate the performance of our approach using three IEEE bus systems and realistic Internet service provider network topology.

Jamming or illegitimate wireless network access interferes with legitimate communication sessions by mimicking the legitimate transmissions and degrades the network performance. In this article, we propose a methodology to detect such attacks by implementing a multiple hypotheses sequential testing-based detection framework with variance and channel state information (CSI)-based algorithms. The detection framework focuses on distinguishing between legitimate and illegitimate transmissions and the nature of illegitimate transmissions with a quaternary hypotheses test. The quaternary hypotheses include no transmission, legitimate node transmission, illegitimate node transmission, and collision-based attack. We first devise a sequential testing problem on a ternary hypothesis problem and then tackle the remaining hypothesis with both variance-based approach and CSI-based approach. We devise algorithms based on the same and compare their performance. We also compare our approach with the generalized Neyman-Pearson approach based on detection speed. In addition, we present a multiple sensor-based approach to further improve the detection performance through soft- and hard-decision combining. We conduct extensive performance evaluations based on both simulated and measurement data. The numerical results show fewer sample size requirements for the proposed algorithms, leading to faster detection.

Multibeam antenna arrays (MBAAs) have the capability to improve the capacity of a wireless network by facilitating simultaneous transmissions to multiple users. However, in practical deployments of wireless personal or local area networks (WPANs/WLANs) where piconet coordinators or access points (PNCs/APs) are deployed with MBAAs, it is quite likely to observe non-uniform node densities in various regions. To optimally utilize MBAAs in such scenarios, concurrent transmission scheduling in WPANs/WLANs is formulated as a multi-objective optimization problem. Then, a practical heuristic transmission scheduler that aims to maximize the number of concurrent communications by dynamically configuring the directions of beams is proposed. In addition, a service tag based fair scheduler is also proposed to achieve weighted fairness in WPANs/WLANs with MBAAs. Our results show that the performance improvements provided by the proposed heuristic scheduler are higher for antennas with lower beamwidths, and as the non-uniformity in the network increases, the traffic supported in the network increases in the range 24-41%, compared to the existing methods. The proposed service tag based scheduler can further improve the network throughput and achieve better fairness at the cost of a few beam direction reconfigurations, as compared to the existing methods and our heuristic scheduler.

Industry 4.0 represents the fourth industrial revolution that will leverage the industrial Internet of Things (IIoT) to introduce adaptive and autonomous systems that can self-heal and self-learn. IIoT aims to promote businesses and industries by realizing intelligent industrialization. However, the constantly surging data volumes that are generated by IIoT environments present security issues like data integrity and system scalability. Blockchain is a promising candidate to address these problems, which offers distributed system design principles. Though blockchain-based IIoT frameworks may have the potential to support the demands and services of next-generation industrial systems, their integration is still constrained by significant challenges in scalability and security. Therefore, blockchain in its original structure with traditional proof-of-work consensus is not suitable for IIoT. To address this, we propose a blockchain architecture that uses a dynamic proof-of-work consensus with a block checkpoint mechanism. The dynamic consensus functions with different mining difficulty levels allow the architecture to efficiently scale with increase in communication traffic of IIoT devices, whereas the checkpoint defines an alternative mechanism to generate the next block hash in the blockchain. To study the scalability and feasibility of the architecture, thorough performance and security analyses are presented, which attest that it can scale and offer enhanced security fidelity with a minimal increase in block mining time.

The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system.

While in-network caching is an essential feature of Information Centric Networks (ICN) for improved content dissemination and reducing the bandwidth consumption at the core of the network, it is prone to many privacy threats. For example, an adversary can passively breach the privacy of a consumer by simply analyzing the different retrieval times for the same content. This paper aims to address this problem of timing analysis attacks by developing privacy-enhancing caching strategies. The proposed caching strategies use two privacy metrics, namely mutual information from information theory and differential privacy, and formulates a privacy enhancing distributed optimization problem with the objective of optimizing the network cost incurred. We efficiently solve the optimization problem by considering it as a$n$-player, non-cooperative game. We show that Nash equilibrium exists for this game and compute it using an iterative best response algorithm. We compare and validate the performance of our approach on realistic network topologies by comparing it with the existing approaches in literature and the global optimal solutions.

Compromised relays in clustered IoT networks can be used to launch attacks that cannot be easily detected using the traditional security algorithms. In this paper, we consider an attack where a compromised relay deliberately drops the packets received from the IoT devices it serves. Such an attack causes the IoT devices to retransmit more frequently, thereby increasing their processing load. As a result, their batteries will drain at a faster rate. The difficulty in differentiating a genuine packet drop event from a malicious packet drop event makes it necessary to develop a novel Intrusion Detection System (IDS) specially tailored for detecting such an attack. The IDS is installed in a special node called a sentinel, which monitors the network. The sentinel also estimates the packet retransmission rate of the IoT devices, a parameter required for the IDS. The effectiveness of the system is demonstrated experimentally on a clustered network.

The constantly growing number of Internet of Things (IoT) devices and their resource-constrained nature makes them particularly vulnerable and increasingly attractive for exploitation by cyber criminals. Current estimates commonly reach the tens of billions for the number of connected 'things'. The heterogeneous capabilities of these devices serve as a motivation for resource sharing among them. However, for effective resource sharing, it is essential that trust be retained in the multitude of pervasive and diverse IoT devices. Remote attestation is a well-known technique used to build such trust. Thus, this paper proposes a blockchain based remote attestation protocol to establish trust between IoT devices. The blockchain offers a secure framework for device registration while the attestation is based on Physical Unclonable Functions (PUF). This combination of technologies results in a tamper resistant scheme with protection against physical and proxy attacks.

With the advancement in network infrastructure and the growth of IoT, more and more vehicular systems are getting connected. Architectures like Information Centric Networks (ICN) are being explored for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and error prone domains. Consumer mobility is implicitly supported in ICN while producer mobility is an important challenge. In this paper, we design a mechanism to support producer mobility using the spatial locality of moving producers and reverse paths of data. Then, we model the consumer (requesting) application as a GI/M/c/N queue which is used to estimate system parameters like content delivery time distribution. We perform simulations using real world traces to evaluate the accuracy and working of our model.

The critical and sensitive nature of data that the Internet-of-Things (IoT) devices produce makes them an attractive target for cyber attacks. Among the various types of attacks, malware is becoming a major concern for the IoT device. This article proposes a remote attestation protocol, hybrid remote attestation, which ensures the high availability of IoT devices during the software attestation process. The proposed attestation technique uses a randomized approach to attest different parts of an IoT device's memory. We use physical unclonable functions (PUFs) to protect the secrets of an IoT device from physical attacks. The security analysis shows that the proposed attestation technique can effectively detect roving malware. Implementation of the proposed protocol on Raspberry Pi and AVR/ARM-based ATMEL microcontrollers and comparison with existing techniques shows that the proposed protocol results in significantly higher availability and lower energy consumption.

Recent developments in IoT have facilitated advancements in the Internet of Vehicles (IoV) with autonomous vehicles and roadside infrastructure as its key components. IoV aims to provide new innovative services for different modes of transport with adaptive traffic management and enables vehicles to broadcast messages to improve traffic safety and efficiency. However, due to nontrusted environments, it is difficult for vehicles to evaluate the credibility of the messages that they receive. Therefore, trust establishment in IoV is a key security concern that is constantly limited by scalability challenges. This article proposes a blockchain-based protocol for IoV using smart contracts, physical unclonable functions (PUFs), certificates, and a dynamic Proof-of-Work (dPoW) consensus algorithm. The blockchain, in conjunction with contracts, provides a secure framework for registering trusted vehicles and blocking malicious ones. PUFs are used to assign a unique identity to each vehicle via which trust is established. Certificates are issued by roadside units that preserve the privacy of vehicles, whereas the dPoW consensus allows the protocol to scale according to the incoming traffic generated by the vehicles. To demonstrate the feasibility and scalability of the proposed protocol, security and performance analyses are presented. A case study is also discussed along with a comparative analysis, which confirms that our protocol can provide superior decentralized trust management for IoV.

Clustering Internet of Things (IoT) networks, to alleviate the network scalability problem, provides an opportunity for an adversary to compromise a set of nodes by simply compromising the relay they are associated with. In such scenarios, an adversary who has compromised the relay can affect the network's performance by deliberately dropping the packets transmitted by the IoT devices and/or by corrupting the packets to be forwarded by the relay. In this way, the adversary can successfully mimic a bad radio channel between the IoT devices and the relay, thereby requiring the IoT devices to retransmit more frequently. Such a strategy increases the processing load on the IoT devices and will drain their batteries at a faster rate. To detect such an attack, we present hybrid intrusion detection systems that rely on the monitoring of uplink and downlink packets transmitted between IoT devices and the relay. Specifically, we compare the observed packet drop probabilities against their long-term expected values. The detection rules proposed originate from the generalized likelihood ratio test, where the adversary parameters are estimated using maximum likelihood estimation. A semi-analytical approach to obtain the expressions for the false alarm probability is presented in order to determine the decision thresholds. Results presented show the effectiveness of the proposed detection systems, demonstrate the impact of the choice of adversary parameters on them, and validate the expressions obtained for the false alarm probability.

Millimeter Wave (mmWave) communications are evolving as a potential and promising technology to address the ever increasing mobile data rate requirements. This paper addresses fair scheduling in directional antenna based mmWave wireless personal and local area networks with non-uniform traffic demand. Due to their small wavelength, mmWave signals are susceptible to blockage. The proposed fair schedulers handle the link blockage problem through relaying, and are capable of providing end-to-end fair bandwidth allocation to relay flows. To achieve differentiated and fair service allocation to various regions of the network while using only limited flow related information, a service tag based scheduler is proposed in this paper. Then, the performance bounds on the minimum throughput and unfairness of this scheduler are obtained. To approximate the performance of the service tag based scheduler while minimizing the control overhead, a heuristic fair scheduler is also proposed. Results from extensive simulations conducted in a mmWave WPAN deployed to support high data rate applications show the performance advantages of the proposed schedulers, compared to existing fair schedulers, in terms of throughput and fairness.

Many next generation Internet architectures exist in the literature for addressing various issues like increasing traffic, mobility and efficient content dissemination. One such emerging fundamental design is Information Centric Networking (ICN). The Pending Interest Table (PIT) is one of the essential components of the ICN forwarding plane responsible for the stateful routing in ICN. Optimal size of the PIT is essential for the efficient performance of the network and the enhanced consumer experience. Therefore, the optimal sizing of the PIT under various network conditions is an important and challenging problem. To this end, this paper models the PIT of a router as a GI/M/c/N queue. The model has (i) a general arrival process to accommodate the diverse nature of traffic, (ii) a service time model which takes into account the caching at the content stores and the mobility of producers, and (iii) a sojourn time distribution which is used to characterize the content delivery time at the consumers. Using the GI/M/c/N queueing model, we formulate an optimization problem to minimize the PIT size while subjecting the interest drop probability to an upper bound. The accuracy of our analytical model is demonstrated using simulations on different Internet Service Provider (ISP) topologies across a wide range of system parameters.

The IEEE 802.11ah standard has been developed to provide Internet access to a large number of devices in the Internet of Things (IoT) and machine-to-machine (M2M) networks. To handle contention from a large number of devices and reduce the collision probability, IEEE 802.11ah partitions nodes into groups by adopting a group- based MAC protocol. The formed groups may consist of nodes with different traffic patterns and hence, the data rate requirements of nodes in a group (and consequently the groups themselves) may not be uniform. To maximize the throughput while minimizing unfairness across groups, this paper formulates fair scheduling in IEEE 802.11ah networks as a multi-objective optimization problem. To maintain fairness among the nodes in a group, contention window size selection of nodes is formulated as an integer programming problem. Since it is difficult to solve these problems in real time, heuristic methods are also proposed. Performance of the proposed methods is evaluated in a dense IoT network and compared with the existing methods. As the number of nodes and groups increase, the proposed method consistently shows a superior performance in terms of fairness, throughput, delay, and power consumption, compared to the existing methods.

IoT can provide many new exciting services in energy management, home and commercial automation, environmental monitoring etc. Data provenance establishes the trust in the origin and location of data. This paper takes an information theoretic approach to solve the problem of data provenance in IoT systems. The proposed protocol uses Physically Unclonable Functions to prove the origin of data and wireless fingerprints derived from the received signal strength indicator (RSSI) measurements to verify the location of the IoT device producing the data. The security analysis of the proposed protocol shows that it is robust against different types of attacks. Experimental results show that the proposed technique can improve the accuracy of detecting attacks by 100% as compared to existing techniques.

The IEEE 802.11ah standard can provide cost-effective Internet access to a large number of devices in newly evolving Internet-of-Things (IoT) and machine-to-machine (M2M) networks. To handle high collision probability caused by a large number of devices, it adopts a group-based protocol at the MAC layer and divides nodes (or sensors) into a number of groups. The formed groups may not be uniform in terms of data rate requirements, since each group is a combination of sensors with different traffic characteristics. To achieve fair resource utilization across the groups which in turn maximizes the channel utilization, this paper formulates fair grouping in IEEE 802.11ah networks as an optimization problem, and we develop a heuristic method to solve the problem in real-time. In addition, to ensure fair channel utilization by the nodes in each group, a contention window selection and adjustment method is proposed. Results from extensive simulations conducted in a dense IoT network show that the proposed fairness model achieves a superior performance than the existing methods in terms of throughput, packet delay, energy efficiency, and fairness.

Mobile Edge Computing can be used to realize the low latency requirements of vehicular networks. However, by compromising the road side units (RSUs), an adversary can introduce an extra delay leading to various problems such as the wastage of edge computing resources and disruption of navigational and safety functions. The compromised RSU can for instance deliberately corrupt the PHY layer payload of the packets to be transmitted to the vehicles. With this simple attack, the adversary would increase latency and through that effect, create serious disruptions. Such an attack can affect many critical delay sensitive applications such as collision avoidance. To detect the presence of such an adversary, we propose a trust based detection system in this paper. Each vehicle transmits a feedback packet about every RSU it has interacted with to a central trusted server. Using the feedback obtained from multiple vehicles, at regular intervals, an aggregated trust value for each RSU in the network is obtained and is compared with a threshold to classify the RSU as authentic or malicious. We also present a mechanism to detect the presence of malicious vehicles reporting false feedback in the network. Simulation results presented demonstrate the effectiveness of the proposed detection mechanism and the impact of the choice of adversary parameters on the detection system.

The development of Internet of Things (IoT) has paved way for the Internet of Vehicles (IoV) and intelligent transportation systems (ITS). Vehicular ad-hoc networks (VANETs) are indispensable for ITS with intelligent vehicles (IV) as their key players. To ensure proper and reliable VANET operation, IVs need secure inter- and intra-network communication with trust and reliability of data (provenance). This paper aims to provide trust management in VANETs by proposing a trustless system model using blockchain and a certificate authority (CA) for registering IVs as well as revoking their registration if need be. Furthermore, to preserve data reliability, this paper uses physical unclonable functions (PUFs). Implementation of DrivMan shows that it is able to establish distributed trust management and enables secure data sharing while preserving the privacy of IVs.

The number of Internet of Things (IoT) devices is expected to grow exponentially in the near future and produce large amounts of potentially sensitive data. The simple and low cost nature of IoT devices makes them an attractive target for spoofing or impersonation attacks. To solve this issue, this paper proposes a two-factor authentication protocol using physically unclonable functions and the characteristics of the wireless signal from an IoT device. The security analysis and results on MICAz motes shows that the proposed protocol can be used as an effective tool to secure IoT systems from spoofing as well as various other attacks. A performance analysis of the proposed protocol shows that it has a significantly lower computational overhead and energy consumption compared to existing techniques.

The Internet of Things (IoT) engulfs a large number of interconnected heterogeneous devices from a wide range of pervasive application areas including health-care systems, energy management, environmental monitoring, and home and commercial automation. Although IoT is considered an enabling technology for a variety of services, it also raises many security and privacy concerns. This article focuses on developing secure protocols for data provenance with authentication and privacy preservation in IoT systems. Protocols for two scenarios are presented, one when an IoT device is directly connected to a wireless gateway and the other when an IoT device is indirectly connected to the wireless gateway through multiple hops of other IoT devices. The proposed protocols use physically unclonable functions along with wireless link fingerprints derived from the wireless channel characteristics between two communicating entities. This results in protocols which are not only efficient in terms of computational complexity and energy requirements but are also safe against various types of attacks including physical and cloning attacks. Experimental results show that in comparison to existing protocols, the proposed protocols are up to 100% more accurate in detecting attacks on data provenance and can save up to 83.8% and 73.5% energy consumption for the IoT devices in terms of CPU and radio energy, respectively.

In this paper, token-based security protocols with dynamic energy-security level tradeoff for Internet of Things (IoT) devices are explored. To assure scalability in the mechanism to authenticate devices in large-sized networks, the proposed protocol is based on the OAuth 2.0 framework, and on secrets generated by on-chip physically unclonable functions. This eliminates the need to share the credentials of the protected resource (e.g., server) with all connected devices, thus overcoming the weaknesses of conventional client-server authentication. To reduce the energy consumption associated with secure data transfers, dynamic energy-quality tradeoff is introduced to save energy when lower security level (or, equivalently, quality in the security subsystem) is acceptable. Energy-quality scaling is introduced at several levels of abstraction, from the individual components in the security subsystem to the network protocol level. The analysis on an MICA 2 mote platform shows that the proposed scheme is robust against different types of attacks and reduces the energy consumption of IoT devices by up to 69% for authentication and authorization, and up to 45% during data transfer, compared to a conventional IoT device with fixed key size.

Device authentication is an essential security feature for Internet of Things (IoT). Many IoT devices are deployed in the open and public places, which makes them vulnerable to physical and cloning attacks. Therefore, any authentication protocol designed for IoT devices should be robust even in cases when an IoT device is captured by an adversary. Moreover, many of the IoT devices have limited storage and computational capabilities. Hence, it is desirable that the security solutions for IoT devices should be computationally efficient. To address all these requirements, in this paper, we present a lightweight and privacy-preserving two-factor authentication scheme for IoT devices, where physically uncloneable functions have been considered as one of the authentication factors. Security and performance analysis show that our proposed scheme is not only robust against several attacks, but also very efficient in terms of computational efficiently.

The concept of smart metering allows real-time measurement of power demand which in turn is expected to result in more efficient energy use and better load balancing. However, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including various personal attributes and activities. Even though several security solutions have been proposed in recent years to address this issue, most of the existing solutions are based on public-key cryptographic primitives, such as homomorphic encryption and elliptic curve digital signature algorithms which are ill-suited for the resource constrained smart meters. On the other hand, to address the computational inefficiency issue, some masking-based solutions have been proposed. However, these schemes cannot ensure some of the imperative security properties, such as consumer's privacy and sender authentication. In this paper, we first propose a lightweight and privacy-friendly masking-based spatial data aggregation scheme for secure forecasting of power demand in smart grids. Our scheme only uses lightweight cryptographic primitives, such as hash functions and exclusive-OR operations. Subsequently, we propose a secure billing solution for smart grids. As compared with existing solutions, our scheme is simple and can ensure better privacy protection and computational efficiency, which are essential for smart grids.

This paper considers the problem of energy-delay tradeoff in wireless networks using aN-policy queueing system based scheduler. A novel analytical model for N-policy queueing system is proposed and tested against other established models and simulation results. Using the model, we argue that N-policy queueing system does not necessarily save more energy as N increases. Based on the analytical and simulation results, we present a scheme for the optimal selection of N for a given arrival and service rate. Simulation results based on applying this framework on sensor networks show that the proposed schemes outperforms previous work in the area. Furthermore, an adaptive N-policy system design is illustrated and shown to save energy while satisfying delay requirements.

With their capability to support high data rates, millimeter-Wave (mmWave) communications are evolving as a promising and potential technology to support high data rate applications in short range networks. This paper addresses the problem of fair scheduling in mmWave wireless personal and local area networks (WPANs/WLANs) to support applications with varying quality of service (QoS) requirements. To ensure fairness while exploiting the spatial reuse facilitated by directional antennas, concurrent transmission scheduling in mmWave WPANs/WLANs is formulated as a multi-objective optimization problem. Two heuristic schedulers are developed to obtain a schedule in real-time. These schedulers first satisfy the minimum QoS requirements of as many flows as possible, and then, allocate the remaining bandwidth to various flows while ensuring long-term and short-term fairness among the flows. Results from extensive simulations conducted in a dense mmWave WPAN show that the proposed fair schedulers provide better fairness and throughput, compared to existing methods.

This paper addresses the problem of developing attestation techniques for Industrial Internet of Things systems. To ensure hardware security, the proposed attestation protocols are based on physically unclonable functions. Moreover, to achieve scalability, the proposed protocols do not calculate the reference checksum for every prover at the verifier, instead they use timing information. Thus, to attest multiple devices in large-scale networks, such as device swarms, the proposed protocols use timing information to detect any unintentional or malicious modification to a device’s memory contents. The analysis on an Atmel micro controller shows that the proposed protocols have a high probability of detecting malware with significantly lower computation overhead.

Devices in the Internet of Things (IoT) produce large amounts of sensitive data. However, the use of the public Internet for data transfer by IoT devices makes them susceptible to cyber attacks. Among these attacks, data tampering or modification attacks to disrupt or bias the states of applications using these data may result in widespread damage and outages. To detect such attacks, this paper proposes an efficient and simple technique to detect data tampering in IoT systems. The proposed mechanism uses a random time hopping sequence and random permutations to hide validation information. We also present a formal security analysis of the proposed protocol. Performance analysis of the proposed protocol shows that it has low computational complexity and is suitable for IoT systems.

Smart grids take advantage of information and communication technologies to achieve energy efficiency, automation, and reliability. These systems allow two-way communications and power flow between the grid and consumers. However, these bidirectional communications introduce several security and privacy threats to consumers. One of the open challenges in this context is user privacy when smart meters (SMs) are used to capture fine-grained energy usage information. Although considerable research has been carried out in this direction, most of the existing solutions invariably introduce computational complexity and overhead, which makes them infeasible for resource constrained SMs. In this paper, we propose a privacy-friendly and efficient data aggregation scheme for dynamic pricing-based billing and demand-response management in smart grids. To the best of our knowledge, this is thefirst paperto address privacy in the context of billing under dynamic electricity pricing. Security and performance analyses show that the proposed scheme offers better privacy protection for electric meter reading aggregation and computational efficiency, as compared to existing schemes.

One of the most important and critical requirements for Internet-of-Things (IoT) based systems is security under limited resources. The simple and low-cost nature of many IoT devices makes them a prime target for physical, side-channel, and cloning attacks. To address this issue, this paper presents an efficient protocol for mutual authentication in IoT systems. The proposed protocol uses a Physical Unclonable Function to provide the desired security characteristics. An analysis of the protocol shows that it is not only robust against different kind of attacks, but also very efficient in terms of memory, computations, energy, and communication overhead.

Many future Internet architectures have been proposed to address issues like increasing traffic, mobility, efficient content dissemination and Named Data Networks (NDN) is emerging as one of the fundamental designs. With the ever-growing mobile data traffic, providing user-mobility has become a necessity. While consumer mobility is implicitly handled in NDN, producer mobility is still of one the main challenges. In this paper we propose a hop-count based forwarding strategy to support seamless producer mobility. The key idea of this strategy is: the router makes a decision based on the number of hops traveled by the interest whether to forward the interest using Forwarding Information Base (FIB) entries or broadcast it. The intuition behind this strategy being two-fold: spatial locality of producer and data packets follow the reverse path of interests in NDN. Using simulations, we evaluate the performance of our proposed approach and compare it with Neighbor Aware Interest Forwarding. We demonstrate that our proposed strategy achieves better throughput in terms of number of interests served while reducing the overall traffic generated.

Directional antennas provide many advantages such as higher gain, increased capacity, longer range, and reduced interference by concentrating radio signal energy in one direction. This paper addresses concurrent transmission scheduling in wireless personal or local area networks deployed with directional antennas. In typical network deployment scenarios, it is quite likely to have non-uniform node densities and traffic demands in various parts of the network. In such situations, to provide load-based service to various parts of the network while aiming to maximize the spatial reuse, this paper proposes a zone-based concurrent data transmission scheduling method. Simulation results show that the proposed method can support a larger number of flows while satisfying a greater fraction of the traffic demands from highly loaded regions, compared to existing methods.

The Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. IoT devices are usually small, low cost, and have limited resources, which makes them vulnerable to physical, side-channel, and cloning attacks. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. To address this issue, we present light-weight mutual authentication protocols for IoT systems based on physical unclonable functions. Protocols for two scenarios are presented, one when an IoT device and server wish to communicate and the other when two IoT devices want to establish a session. A security and performance analysis of the protocols shows that they are not only robust against different types of attacks, but are also very efficient in terms of computation, memory, energy, and communication overhead. The proposed protocols are suitable for real time applications and are an attractive choice for implementing mutual authentication in IoT systems.

Base stations equipped with resources to harvest renewable energy are not only environment-friendly but can also reduce the grid energy consumed, thus bringing cost savings for the cellular network operators. Intelligent management of the harvested energy can further increase the cost savings. Such management of energy savings has to be carefully coupled with managing the quality of service so as to ensure customer satisfaction. In such a process, there is a trade-off between the energy drawn from grid and the quality of service. Unlike prior studies which mainly focus on network energy minimization, this paper proposes a framework for jointly managing the grid energy savings and the quality of service (in terms of the network latency), which is achieved by downlink power control and user association reconfiguration. We use a real BS deployment scenario from London, U.K., to show the performance of our proposed framework and compare it against existing benchmarks. We show that the proposed framework can lead to around 60% grid energy savings as well as better network latency performance than the traditionally used scheme.

Software piracy is a common occurrence, and a significant fraction of the personal computers have some pirated software installed. Cyber-criminals often use pirated software as a vector to spread malware by bundling malicious software with the pirated software. This paper presents the results of a case study that aims to quantify the incidence of malware in pirated software that come bundled with new personal computer purchases. The paper also evaluates the types of malware that are present in the samples in our case study, and the locations in the file system where these malware are detected. The results show that 63% of the samples procured for the case study showed presence of malware and the incidence of malware varies with the geographical location where the sample was procured. Our results also indicate that Trojans and Hacktools are the most prevalent families of malware in our samples.

Devices that monitor and measure various system parameters or physical phenomena form an integral part of cyber-physical systems. Such devices usually operate continuously and gather important data that is often critical for the operation of the underlying system. Thus, it becomes important to understand and detect abnormal or malicious device behavior, false injection of data by an adversary, or other security threats that may lead to incorrect measurement data. This paper addresses the problem of detection of anomalies in diurnal traffic volume data in an intelligent transportation system. The proposed approach leverages the statistical properties of the data to perform anomaly detection by calculating the `local density' of the data points. Anomalous behavior in the traffic volumes reported by road segments is calculated based on sparse local density of the data points. Our approach for detecting anomalies does not require any information about the outside factors which might have influenced the data. The proposed approach has been evaluated on attacks simulated on transportation data collected by the New York State Department of Transportation. The proposed approach also extends to other cyber-physical systems where the monitored data exhibits diurnal patterns.

One of the major issues in the deployment of solar powered base stations (BSs) is to dimension the photovoltaic (PV) panel and battery size resources, while satisfying outage constraints with least cost. The fundamental step in this dimensioning is to evaluate the power outage probability associated with a particular configuration of PV panel and battery size. This paper addresses this issue by first proposing an analytic model to evaluate the power outage probability of a solar powered BS. The proposed model accounts for hourly as well as daily variation in the harvested solar energy as well as the load dependent BS power consumption. The model evaluates the steady state probability of the battery level, which is then used to estimate the BS power outage probability. Next, given a tolerable power outage probability, we address the problem of obtaining the cost-optimal PV panel and battery dimensions for the BS. The proposed model and the framework have been evaluated using empirical solar energy data for geographically diverse locations.

With the expected explosion in the number of devices in the Internet-of-Things (IoT), the availability of spectrum for these devices to connect to the network is a challenging problem. A possible solution to this problem is the use of opportunistic machine-to-machine (M2M) communications where IoT devices exploit idle periods of primary users (i.e., users with higher priority on spectrum usage) to transmit their data. The feasibility of such opportunistic M2M communication depends on the temporal characteristics of the availability of unused spectrum. Considering the unlicensed bands where Wi-Fi devices are the primary users, we present a BMAP/G/1/nK queue-based model to characterize the duration and frequency of the periods available for opportunistic M2M communications. Our results show that M2M devices may co-exist with W-Fi networks, and even in Wi-Fi networks with high loads, there are adequately long and frequent idle periods that can be used to support opportunistic M2M communications.

Solar powered cellular base stations are emerging as a key solution in green cellular networks. A major challenge in the design of such a base station (BS) is finding the optimal cost configuration of the photo-voltaic (PV) panel size and number of batteries which meets a tolerable outage probability with the least cost. One of the fundamental steps in this process is to calculate the outage probability associated with a particular PV panel size and battery size configuration. To address this issue, this paper proposes an analytic model to evaluate the outage probability of a solar powered BS. The proposed model factors in the daily and hourly variations in the harvested solar energy and the traffic dependent BS load, and develops a discrete-time Markov process to model the battery level and thus the outage probability of the BS. Simulation results with empirical solar irradiance data for three different locations are used to validate the proposed model and demonstrate its accuracy.

Machine to machine (M2M) communications are expected to form one of the fundamental building blocks of the future Internet of Things (IoT). In view of the scarcity of spectrum and the service requirements of traditional users, providing network access to the extremely large number of devices in IoT and M2M scenarios is one of the fundamental problems for network designers and operators. As a possible solution to this issue, this paper explores the possibility of using the unlicensed industrial, scientific and medical (ISM) band for supporting M2M communications while co-existing with traditional users of this band. Since IEEE 802.11 or WiFi based networks are the most common networking technology in the ISM band, this paper presents an evaluation of “white spaces” in WiFi networks (i.e. periods where the WiFi network is not using the channel) that may be used opportunistically for M2M communications. Using a MMPP/G/1/K queue to model the operation of a WiFi access point, we characterize the WiFi white spaces in terms of their frequency, duration, and their probability distribution. Our results show that WiFi white spaces provide considerable transmission opportunities that may be exploited for M2M communications.

With the growing interest in the use of autonomous computing, sensing and actuating devices for various applications such as smart grids, home networking, smart environments and cities, health care, and machine-to-machine (M2M) communication has become an important networking paradigm. However, in order to fully exploit the applications facilitated by M2M communications, adequate support from all layers in the network stack must first be provided in order to meet their service requirements. This paper presents a survey of the requirements, technical challenges, and existing work on medium access control (MAC) layer protocols for supporting M2M communications. This paper first describes the issues related to efficient, scalable, and fair channel access for M2M communications. Then, in addition to protocols that have been developed specifically for M2M communications, this paper reviews existing MAC protocols and their applicability to M2M communications. This survey paper then discusses ongoing standardization efforts and open problems for future research in this area.

The deployment of cellular network infrastructure powered by renewable energy sources is gaining popularity as an avenue to provide coverage in areas without reliable grid power and also as a means to reduce the environmental impact of the telecommunications industry. To facilitate the deployment of such networks, this paper addresses the problem of resource provisioning and dimensioning solar powered base stations in terms of the required battery capacity and photo-voltaic (PV) panel sizing. The paper first develops a framework for evaluating the outage probability associated with a base station at a given location as a function of the battery and panel size, by using the solar energy and traffic profiles as inputs. A model is then proposed to evaluate the optimal battery and PV panel sizing, subject to the desired limit on the worst month outage probability. The proposed framework for dimensioning the base station's energy resource requirements has been evaluated using real solar irradiation data for multiple locations.

Hardware techniques such as dynamic voltage and frequency scaling may be used to reduce the energy consumption of network interfaces and achieve load-proportional energy usage. These techniques slow down the operation of the hardware and thus their power savings come at the cost of increased packet delays. This paper presents a methodology to address the energy-delay tradeoff while achieving load-proportional energy usage in wireless networks. The proposed system uses a pipelined implementation of the functional blocks of the medium access control (MAC) layer. Each functional block has its own job queue and is treated as an individual system with an independent clock. The clock frequency of each functional block is dynamically selected based on the length of its job queue. While the pipelined implementation reduces the MAC layer processing delays, the use of queue-length based frequency scaling provides load-proportional energy usage and enhances the overall stability of the system. The performance of the proposed system has been verified through extensive simulations.

The use of synchrophasor data for observation and control is expected to enhance the operation and efficiency of the next generation of power transmission systems. The synchrophasor measurement data is usually transferred over public domain networks such as the Internet, thereby making it susceptible to a number of attacks. This paper focuses on packet dropping or gray hole attacks on networks carrying synchrophasor data and develops a mechanism to detect such attacks. Our solutions is based on exploiting the patterns and correlation between packet delays and packet losses due to congestion in order to differentiate naturally occurring packet drops in the Internet from packet drops by gray hole attacks. The effectiveness of the proposed mechanism has been verified using simulations.

Existing wireless medium access control (MAC) protocols provi reliability against corrupted packets by providing mechanisms for packet error detection and retransmission. The efficiency of existing mechanisms for providing reliability is usually low since they require the entire packet to be retransmitted even though only parts of it may have been corrupted. To address this issue, this paper presents a MAC protocol with an efficient packet recovery mechanism for packets corrupted due to both channel errors and collisions. The proposed MAC protocol first determines the cause of the errors in a packet and then uses the acknowledgment (ACK) packets to provide feedback on the sections of the packets that have errors. To minimize the packet recovery time, the proposed MAC protocol allows the sender to retransmit the corrupted sections of the packet immediately, without requiring a new channel access. Using simulations, it is shown that the proposed MAC protocol has higher efficiency and increases the achieved throughput.

Wireless medium access control (MAC) protocols usually provide reliability in the presence of packet errors. The efficiency of these reliability mechanisms is generally low since they require the entire packet to be retransmitted even though only parts of it may have been corrupted. To address this issue, this paper presents an efficient mechanism for the recovery of packets corrupted due to both channel errors and collisions. The proposed mechanism first determines the cause of the errors. Next, the symbols with errors are isolated by using the error vector magnitude (EVM) of received symbols as the feature for detection. Using explicit feedback about which blocks of symbols have errors, only the erroneous blocks are then retransmitted. Our results show that the proposed mechanism increases the efficiency of the MAC protocol by providing higher throughput.

Distributed, throughput-optimal medium access control (MAC) protocols have recently been proposed for wireless networks. While the performance of these protocols is well understood in terms of the throughput, simulation studies have shown that their delay performance is very sensitive to the protocol's parameter values. To address the problem of selecting parameters that minimize the average packet delay, this paper first develops a queueing model to evaluate the delay of a class of discrete-time, throughput-optimal MAC protocols. This model is then used to derive the optimal parameter settings for the MAC protocol. Simulation results are presented to validate the delay model and the parameter selection methodology.

From the perspective of energy efficiency, a major drawback of existing wireless networks is that the power consumption rate of the networking hardware stays at levels close to the maximum, even when the offered traffic load is low. To address this issue, this paper presents a mechanism for load proportional energy usage in wireless networks. The proposed mechanism is based on changing the operating clock frequency of the network interface cards as a function of the offered load, in order to reduce to energy consumption. To this end, we propose a frequency selection mechanism that tries to ensure the stability of the queues while minimizing the energy consumption. The proposed mechanism has been evaluated through simulations.

In wireless networks without nodes equipped with multiple antennas, cooperative Multiple Input Multiple Output (MIMO) transmissions may be used to harness diversity gains. In general, diversity gains are larger if more nodes are involved in the transmission. However, a transmission policy that maximizes the diversity gain or throughput need not maximize the stability region, since queues at the nodes may grow while waiting for a sufficient number of nodes to become available. To address this issue, this paper develops a mechanism for maximizing the throughput while reducing the energy consumption and maintaining queue stability. We develop a sufficient condition that ensures the throughput optimality of a stable transmission policy and then use it to design a distributed, dynamic threshold based Medium Access Control (MAC) protocol for cooperative MIMO transmissions. The MAC protocol requires only limited local information for its operation. Simulation results are provided to evaluate the performance of the proposed protocol and compare it against regular point-to-point and existing cooperative MIMO MAC protocols. The results show that the proposed scheme can provide considerable gains in the throughput and energy savings compared to cooperative MIMO based on fixed number of cooperating nodes.

Energy harvesting is one of the promising solutions to the problem of limited battery capacity in many wireless devices. This paper addresses the problem of system design of energy harvesting capable wireless devices in terms of the required sizes for energy and data buffers, as well as the size of the harvester, for given delay and loss requirements. We analyze the performance of an energy harvesting node, considering a stochastic model that takes into account energy harvesting and event arrival processes. We derive closed-form expressions for the probability of event loss and the average queueing delay. Our event-driven continuous time simulations validate our analytical results. Employing these results, we provide a near-optimal approach to the design of the system in terms of sizing the energy harvesting device, the energy storage, and the event queue capacity.

There are two causes of packet losses during a wireless transmission: losses caused by collisions and losses caused by poor channel conditions. The throughput and spatial reuse of IEEE 802.11 based wireless networks, as well as the effectiveness of the rate adaptation algorithms they use, is adversely affected by their inability to determine the real cause of a packet loss. To address this issue, this paper proposes a mechanism based on Error Vector Magnitude (EVM) to discern random channel errors from collisions in wireless networks. The proposed mechanism is based on first developing an analytic model to characterize the EVM of a packet in the presence and absence of a collision. A threshold based classifier is then proposed that selects the threshold value such that the crossover error rate is achieved. Simulation results are presented to demonstrate the accuracy of the proposed collision detection mechanism.

Energy harvesting and cooperative communication are promising solutions to overcome the power limitations of Wireless Sensor Networks (WSNs) comprising of battery-powered nodes. In order to maximize the efficiency of such systems, measured in terms of packet delivery ratio achieved over time, efficient scheduling algorithms need to be designed. In particular, relay usage scheduling is critical for addressing the trade-off between energy consumption and efficiency in the network. However, the stochastic nature of the recharge and traffic generation processes at the sensor nodes, along with partial state information availability about neighboring nodes, makes the transmission and relay scheduling problem quite challenging. To address this problem, we model the system using a stochastic framework, and formulate the scheduling problem at source sensor node, when only partial state information about the relay is available at the source, as a Partially Observable Markov Decision Process (POMDP). We characterize an approximate solution to the optimality equations, which provides us with useful insights into the system dynamics. We observe that the structure of optimal policy is quite sensitive to system parameters, which makes it unsuitable for practical deployment. Therefore, we design a simple and practical threshold based relay scheduling policy, and show using simulations that it achieves close to optimal performance.

This paper presents a queueing model for the polling based service classes of WiMAX/IEEE 802.16 based wireless networks. Models are presented for both single channel and multiple channel (OFDMA based) operations. The models evaluate the MAC layer packet delays as a function of various system parameters.

While decentralized medium access control (MAC) protocols are more popular in wireless environments, cluster based sensor networks are particularly amenable to centralized, polling based protocols. In this paper we present an analytic model to evaluate the performance of polling based MAC protocols for wireless networks in terms of the packet delay, buffer overflow rates and energy consumption. We show that polling based protocols can outperform popular decentralized MAC protocols. Simulation results are presented to validate our model and conclusions.

A large set of potential applications being designed for intelligent transportation systems (ITSs) depends on the broadcasting of information and control packets by roadside infrastructure points to vehicles in their vicinity. This paper considers the broadcast capacity of broadcast schemes and evaluates and compares the broadcast capacity of strategies based on time splitting, frequency splitting, and superposition coding. Frequency splitting is shown to always dominate time splitting, and the conditions under which superposition coding dominates the other two are derived. For these regimes, it is shown that the broadcast capacities associated with superposition coding are optimal. A proportionally fair algorithm for scheduling broadcast packets is then proposed, and its performance is compared against that of other schedulers.

This paper addresses the question of throughput guarantees through distributed scheduling in sensor networks with relay based cooperative communications. We prove that in a single frequency network with bidirectional, equal power communication and low complexity distributed maximal scheduling attains a guaranteed fraction of the maximum throughput region in arbitrary wireless networks. We also show that the guarantees are tight in the sense that they cannot be improved any further with maximal scheduling. Simulation results are also provided to show the performance of a distributed, maximal scheduling algorithm under different network settings.

In this paper, we formulate an analytical model to characterize the spread of malware in decentralized, Gnutella type peer-to-peer (P2P) networks and study the dynamics associated with the spread of malware. Using a compartmental model, we derive the system parameters or network conditions under which the P2P network may reach a malware free equilibrium. The model also evaluates the effect of control strategies like node quarantine on stifling the spread of malware. The model is then extended to consider the impact of P2P networks on the malware spread in networks of smart cell phones.

This paper considers wireless sensor networks (WSNs) with energy harvesting and cooperative communications and develops energy efficient scheduling strategies for such networks. In order to maximize the long-term utility of the network, the scheduling problem considered in this paper addresses the following question: given an estimate of the current network state, should a source transmit its data directly to the destination or use a relay to help with the transmission? We first develop an upper bound on the performance of any arbitrary scheduler. Next, the optimal scheduling problem is formulated and solved as a Markov Decision Process (MDP), assuming that complete state information about the relays is available at the source nodes. We then relax the assumption of the availability of full state information, and formulate the scheduling problem as a Partially Observable Markov Decision Process (POMDP) and show that it can be decomposed into an equivalent MDP problem. Simulation results are used to show the performance of the schedulers.

Sensors equipped with energy harvesting and cooperative communication capabilities are a viable solution to the power limitations of Wireless Sensor Networks (WSNs) associated with current battery technology. However, the optimal scheduling of transmissions in such networks is challenging due to the requirement of complete state information of the relay nodes. This paper addresses the problem of transmission scheduling in such networks when onlypartialstate information about the relays is available at the source. We formulate the scheduling problem as a Partially Observable Markov Decision Process (POMDP), and show that it can be decomposed into an equivalent Markov Decision Process (MDP) problem. Simulation results are used to show the performance of the scheduler.

This paper addresses the question of throughput guarantees through distributed scheduling in wireless sensor networks (WSNs) with relay based cooperative communications. We prove that in a single frequency network with bidirectional, equal power communication, low complexity distributed maximal scheduling attains a guaranteed fraction of the maximum throughput region in arbitrary wireless networks. We also show that the guarantees are tight in the sense that they cannot be improved any further with maximal scheduling. Simulation results are also provided to show the performance of a distributed, maximal scheduling algorithm under different network settings.

Sensors equipped with energy harvesting capabilities are a viable solution to the limitations of current battery technology associated with wireless sensor networks (WSNs). This paper addresses the problem of developing energy efficient transmission strategies for WSNs with energy harvesting capabilities and cooperative transmission options. Taking into account the energy harvesting capabilities of the sensors, decision policies are developed to determine the transmission mode to use at a given instant of time in order to maximize the quality of coverage. The problem is formulated in a Markov Decision Process (MDP) framework and an upper bound on the performance of arbitrary policies is determined.

Energy harvesting is one of the most promising solutions for the enhancing the lifetime of sensor networks by overcoming the limitations of current batter technology. This paper investigates the performance of scheduling strategies for sensor networks with energy harvesting. The problem of selecting the power level at which a sensor should transmit is formulated as a Markov Decision Process (MDP) and the performance of the transmission policy thus derived is compared with that of an energy balancing policy as well as an aggressive policy. Our results show that the quality of coverage associated with the MDP formulation outperforms the other policies.

The residual power levels of the nodes in a wireless sensor network determine its important performance metrics like the network lifetime, coverage, and connectivity. In this paper, we present a general framework to model the availability of power at sensor nodes as a function of time, based on models for population dynamics in biological studies. Models are developed for sensors with and without battery recharging and expressions are derived for the network lifetime as well as the distribution and moments of random variables describing the number of sensors with different levels of residual energy as a function of time. The model is also extended to the case where new sensors are periodically added to the network to substitute older sensors that have expended their energy. Finally, the effect of the packet arrival rates and a sensor's geographical location are modeled. Simulation results to verify the accuracy of the proposed models are presented.

This paper addresses the problem of developing energy efficient transmission strategies for Body Sensor Networks (BSNs) with energy harvesting. It is assumed that multiple transmission modes that allow a tradeoff between the energy consumption and packet error probability are available to the sensor nodes. Taking into account the energy harvesting capabilities of the nodes, decision policies are developed to determine the transmission mode to use at a given instant of time in order to maximize the quality of coverage. The problem is formulated as a Markov Decision Process (MDP) and the performance of the transmission policy thus derived is compared with that of energy balancing as well as aggressive policies. An upper bound on the performance of arbitrary policies, and lower bounds specific to energy balancing and aggressive policies are derived.

Mobility in vehicular networks naturally leads frequent handoffs and reassociations between the vehicles and roadside access points. The overhead due to these reassociation comes in the form of the bandwidth and delays associated with the handoff related packets that are exchanged between a vehicles and the access point. This paper derives an information theoretic lower bound on the Medium Access Control (MAC) layer overhead associated with reassociations caused due to node mobility. An efficient MAC protocol is then proposed that reduces the handoff delays. Simulations are used to demonstrate the superior performance of the proposed protocol in terms of the throughput and packet latency.

This paper presents a queuing model to evaluate the latency associated with file transfers or replications in peer-to-peer (P2P) computer systems. The main contribution of this paper is a modeling framework for the peers that accounts for the file size distribution, the search time, load distribution at peers, and number of concurrent downloads allowed by a peer. We propose a queuing model that models the nodes or peers in such systems as M/G/1/K processor sharing queues. The model is extended to account for peers which alternate between online and offline states. The proposed queuing model for the peers is combined with a single class open queuing network for the routers interconnecting the peers to obtain the overall file transfer latency. We also show that in scenarios with multipart downloads from different peers, a rate proportional allocation strategy minimizes the download times.

This paper proposes a mechanism for detecting session hijacking attacks in wireless networks. The proposed scheme is based on using a wavelet based analysis of the received signal strength. We first develop a model to describe the changes in the received signal strength of a wireless station during a session hijack, while the received signal is embedded in colored noise caused by fading wireless channels. An optimal filter is then designed for the purpose of detection. We show that using a Wavelet Transform (WT), the colored noise with complex Power Spectral Density (PSD) in our case can be approximately whitened. Since a larger Signal to Noise Ratio (SNR) increases the detection rate and decreases the false alarm rate, the SNR is maximized by analyzing the signal at specific frequency ranges. The detection mechanism is validated using both simulation and experimental results. The detector is shown to be reliable, computationally inexpensive and have minimal impact on the network performance.

The increasing popularity and success of web-based peer-to-peer (P2P) systems for streaming video applications make them likely candidates for injecting large volumes of traffic in the emerging WiMAX networks. This paper develops a lightweight mechanism for P2P streaming in WiMAX networks that significantly reduces the load on the network and improves the scalability of the streaming system. The proposed system uses the broadcast mechanism provided in the IEEE 802.16 mechanism for providing the scalability, without breaking the P2P semantics. The scalability of the proposed system is analytically evaluated and also quantified using simulations. Our results show that the degree of improvement in the performance of the proposed system is lower bounded by the average number of peers served by an Access Service Network Gateway in the WiMAX networks.

A number of important characteristics of wireless sensor networks such as the lifetime, connectivity and coverage are determined the residual power levels of the nodes in the network. This paper presents a general framework for modeling the availability of power at sensor nodes as a function of time. Models are developed for sensors with and without battery recharging and expressions are derived for the network lifetime as well as the distribution and moments of random variables describing the number of sensors with different levels of residual energy as a function of time. Finally, the effect of the packet arrival rates and a sensor's geographical location are modeled.

This paper develops a distributed, threshold based MAC protocol for cooperative multi input multi output (MIMO) transmissions in distributed wireless systems. The protocol uses a thresholding scheme that is updated dynamically based on the queue length at the sending node to achieve low power transmissions while ensuring stability of the transmission queues at the nodes. Simulation results are provided to evaluate the performance of the proposed protocol and compare it against regular point to point as well as fixed group size cooperative MIMO MAC protocols.

The importance of border gateway protocol (BGP) as the primary interautonomous system (AS) routing protocol that maintains the connectivity of the Internet imposes stringent stability requirements on its route selection process. Accidental and malicious activities such as misconfigurations, failures, and worm attacks can induce severe BGP instabilities leading to data loss, extensive delays, and loss of connectivity. In this work, we propose an online instability detection architecture that can be implemented by individual routers. We use statistical pattern recognition techniques for detecting the instabilities, and the algorithm is evaluated using real Internet data for a diverse set of events including misconfiguration, node failures, and several worm attacks. The proposed scheme is based on adaptive segmentation of feature traces extracted from BGP update messages and exploiting the temporal and spatial correlations in the traces for robust detection of the instability events. Furthermore, we use route change information to pinpoint the culprit ASes where the instabilities have originated.

Polymorphic computer worms are characterized by their ability to change their byte sequence as they replicate and propagate, thereby aiming to thwart intrusion detection systems (IDSes). In this letter, we propose a model based on coevolution of biological quasi-species to characterize the propagation of polymorphic worms and the effect of IDSes on their dynamics. The model is used to derive the conditions required for the IDS to contain the worm. The model is validated using simulations.

This paper investigates the performance of IEEE 802.11 in multi-hop scenarios and shows how its aggressive behavior can throttle the spatial reuse and reduce bandwidth efficiency. An adaptive, layer-2, distributed coordination scheme for 802.11 using explicit medium access control (MAC) feedback is then proposed to pace the transmissions on adjacent nodes, thereby assisting the MAC protocol to operate around its saturation state while minimizing resource contention. Simulation results show that the proposed scheme outperforms the original 802.11 MAC.

This paper investigates how to support multicasting in wireless ad hoc networks without throttling the dominant unicast flows. Unicast flows are usually congestion-controlled with protocols like TCP. However, there are no such protocols for multicast flows in wireless ad hoc networks and multicast flows can therefore cause severe congestion and throttle TCP-like flows in these environments. Based on a cross-layer approach, this paper proposes a completely-localized scheme to prevent multicast flows from causing severe congestion and the associated deleterious effects on other flows in wireless ad hoc networks. The proposed scheme combines the layered multicast concept with the routing-based congestion avoidance idea to reduce the aggregated rate of multicast flows when they use excessive bandwidth on a wireless link. Our analysis and extensive simulations show that the fully-localized scheme proposed in this paper is effective in ensuring the fairness of bandwidth sharing between multicast and unicast flows in wireless ad hoc networks.

This paper considers the performance of the polling based service classes of IEEE 802.16 based broadband wireless access networks and develops queueing models to evaluate their delay distributions and loss rates. Both single and multiple carrier OFDMA operations are considered and models are proposed for two polling strategies. The models can be used to provide probabilistic service guarantees and explore the impact of various system parameters on the performance, thereby aiding in system design. The models are verified using simulations.

This paper develops a mechanism for detecting session hijacking attacks in wireless networks. The proposed scheme is based on detecting abrupt changes in the strength of the received signal. We first develop a mathematical model to describe the signal strength during a session hijacking: a step function signal, which represents the abrupt jump in the signal strength, imbedded in colored noise, which is caused by fading wireless channels. An optimal filter is designed for the purpose of detection. We show that using a wavelet transform (WT), the colored noise with complex power spectral density (PSD) in our case can be approximately whitened. Since larger signal to noise ratio (SNR) increases the detection rate and decreases the false alarm rate, we maximize the SNR by analyzing the signal at specific ranges of frequency. We validate the detection mechanism by simulation and experimental results.

In this paper we propose a distributed system for facilitating cooperative MIMO transmissions in networks without multiple antenna devices. MIMO diversity is achieved by employing groups of nodes in the vicinity of the source and destination to help with the transmission. The distributed sending nodes are assumed to have different carrier frequency offsets (CFO). Space-time block codes (STBC) and code combining are used to utilize spatial diversity. The estimation of multiple CFO and detector for STBC-coded data under multiple CFO are provided. The BER of the proposed system is shown and discussed. We also consider the energy consumption and compare it with other cooperative designs.

A large set of potential applications being designed for intelligent transportation systems (ITS) depend on the broadcasting of information and control packets by roadside infrastructure points to vehicles in their vicinity. This paper considers the transport capacity of broadcast schemes and evaluates and compares the transport capacity of strategies based on time-splitting, frequency-splitting and superposition coding. A proportionally fair broadcast scheduling algorithm is then proposed and its performance compared against other schedulers.

Underwater acoustic sensor networks face unique challenges in the design and development of communication and network protocols, because of the inherently different characteristics of water as a medium for signal propagation. In the mobile sink architecture, a mobile sink that traverses the network to transfer non delay-sensitive data from the sensors directly and avoid multi-hop transmissions. An area partitioning algorithm is proposed in this paper to divide the network in regions to minimize the traveling distance of the sink and the formation of clusters that maximize the throughput. A transmission mechanism based on superposition coding is developed to increase the throughput of downlink control messages to the sensors. Finally, a MAC protocol is developed to facilitate the transmissions.

Prediction of rapidly time varying fading channel conditions enables adaptive data transmissions in wireless systems, which in turn improves the quality of service for end users and reduces the power consumption for data transmissions. Most of the existing long range prediction methods for fast fading in wireless networks use autoregressive (AR) models and make the assumption that the input fading signal is stationary and wireless channel parameters vary slowly (S. Semmelrodt and R. Kattenbach, 2003). In this paper, we provide a method to predict the non-stationary received signal strength in a more realistic and fast varying wireless environment, using multiresolution wavelet analysis. We first use discrete wavelet decomposition (DWT) to decompose the signal strength trace into components at different scales, then use AR and linear regression models to predict small, medium and large scale fading components respectively, and finally synthesize the output signal of our prediction algorithm. By properly choosing the wavelet basis, we map the non-stationary signal strength trace into stationary wavelet detail coefficients and use them as input to the AR predictor at different scales. Longer prediction range is easily achieved by choosing the appropriate maximum decomposition scale, while still achieving low prediction error. Our experimental results shows that our wavelet based algorithm outperforms existing time-domain AR prediction methods in terms of both prediction accuracy and computational complexity.

Prediction of rapidly time variant fading channel conditions enables adaptive data transmission in wireless systems, which in turn improves the quality of service for end users and reduces the power consumption for data transmissions. In this paper, we construct an accurate, low-complexity, on-line prediction mechanism for the long range prediction of wireless link quality. Our method is independent of the propagation environment and distance between user nodes and the access points. The proposed method uses past measurements of the received signal strength as its input, and uses a combination of segmentation, filtering and regression to predict the future trend in the received signal strength. An adaptive windowing mechanism is designed to adapt to abrupt changes in the data trace, which considerably reduces the prediction error. The algorithm is tested on real life networks in diverse environments. The prediction results are compared with one of the best existing channel prediction algorithm. We show that our algorithm can be used as a robust and comparatively more accurate predictor.

This paper presents a new protocol for vehicle to roadside networks and presents an analysis of the handoff related overhead of general MAC protocols for these scenarios. The proposed protocol's features include the elimination of hidden nodes, prioritized and fast handoff, fairness among nodes and optimal choice of backoff parameters. The analysis presented in the paper derives an information theoretic lower bound on the MAC layer overhead associated with node reassociations. Simulation results are used to demonstrate the superior performance of the proposed protocol in comparison to existing protocols.

This paper addresses the problem of data acquisition in ad hoc and sensor networks with mobile sinks and proposes a protocol based on swarm intelligence, SIMPLE, to route data in such environments. The proposed protocol is based on a swarm agent that integrates the residual energy of nodes into the route selection mechanism and maximizes the network's lifetime by evenly balancing the residual energy across nodes and minimizing the protocol overhead. The protocol is robust and scales well with both the network size and in the presence of multiple sinks. An information theoretic lower bound on the protocol overhead associated with the swarm agent advertisement is also obtained. SIMPLE is also shown to have a lower message complexity compared to similar algorithms previously proposed in literature. Simulation results are used to verify SIMPLE's performance and robustness and also to demonstrate its superior performance over existing algorithms.

Being the primary interdomain routing protocol, border gateway protocol (BGP) is the singular means of path establishment across the Internet. Therefore, misconfiguration errors in BGP routers result in failure to establish paths which in turn can cause several networks to become unreachable. In this paper, we first analyze data from recent BGP tables to show that misconfiguration errors occur very frequently in the Internet today. We then show theoretically and using real-world events the impact of these errors on routing stability. A scheme for real-time isolation of large-scale BGP misconfiguration events is then proposed in this paper. Our methodology is based on statistical techniques and is evaluated using data from past wellknown misconfiguration events. We show the effectiveness of our method as compared to the current state-of-the-art.

In wireless communications, shadow fading can cause at least 6 dB power loss for 10% of the time [1]. Early detection of shadow fading plays an important part in facilitating the design of adaptive data transmission schemes. We propose an accurate, on-line detection mechanism to detect a receiver entering or leaving shadow regions using simply signal strength measurements. The method is based on wavelet decomposition of signal strength time series into independent fading components. Our measurements indicate that fast fading signals have a scale invariant nature. This scale invariance of fading signals is destroyed when shadow fading, which is log-normal distributed and is independent of fast fading components, is added to the signal. An online detection mechanism is then proposed which exploits this phenomenon. Real measurements of signal strength traces are used to validate the detection mechanism.

Cooperative MIMO can achieve higher energy savings and lower delays in distributed systems by allowing nodes to transmit and receive information jointly. In this paper, we develop a new MAC protocol for enabling packet transmissions using cooperative MIMO. The paper also develops analytical models for evaluating the packet error probability, energy consumption and packet delays associated with the proposed MAC protocol. The analysis is validated against simulation results using the NS- 2 simulator. Our results show that the proposed MAC protocol has lower delays and lower energy consumption as compared to regular point to point MAC protocols.

Measurements have shown that network path failures occur frequently in the Internet and physical link failures can cause network instability in large scale and severity. Thus early anomaly detection mechanisms are of great importance. In this paper, we propose a Bayesian approach for time efficient link failure detection using border gateway protocol (BGP) update message traces. The detection is done using an automated mechanism to label, train and classify the network status based on features extracted from BGP traces. In addition to detecting temporal changes in these features, our scheme augments its accuracy by including information on the spatial correlation of the route updates in the decision process. We validate our approach by testing the proposed mechanism on real BGP traces collected during three typical network outage events caused by link failures.

Recent outbreaks of virus and worm attacks targeted at cell phones have bought to the forefront the seriousness of the security threat to this increasingly popular means of communication. In this paper, we develop an analytic framework for modeling the dynamics of malware propagation in networks of mobile smart phones. We then characterize the conditions under which the network may reach a malware free equilibrium and derive the necessary conditions for its global asymptotic stability. The model accounts for malware transfers through a number of communication paradigms including the Internet, the telephone network, Bluetooth and WiFi in addition to accounting for the mobility of cell phones and the impact of the heterogeneous environments they pass through on the malware dynamics.

In sensor networks, MAC protocols based on time division multiple access (TDMA) with wakeup and sleep periods have attracted considerable interest because of their low power consumption and collision free operation. In this paper, we develop analytic models to evaluate the performance of such protocols. The model presented in this paper characterizes the queueing delays associated with the MAC layer as well as the energy consumed at MAC layer, by modeling the system as a queue with general service time distribution with both polling and vacations. The analysis is validated by comparison with simulation results using the NS-2 simulator. Our results show that polling based TDMA with sleep and wakeup cycles has lower delays and lower energy consumption as compared to SMAC.

Recent outbreaks of virus and worm attacks targeted at cell phones have have bought to the forefront the seriousness of the security threat to this increasingly popular means of communication. The ability of smart cell phones to communicate through both the Internet and the telecom networks along with the presence of a number of communication interfaces makes them vulnerable to attacks from a number of sources which can then propagate at extremely fast rates. In this paper we develop an analytic framework for modeling the dynamics of malware propagation in networks of smart phones that specifically accounts for the mobile nature of these devices. We also characterize the conditions under which the network may reach a malware free equilibrium and derive the necessary conditions for its global asymptotic stability. The model accounts for malware transfers through the Internet and peer to peer networks, through the telephone network and through Bluetooth and WLAN interfaces.

LEACH (low energy adaptive clustering hierarchy) (W. Heinzelman et al., 2000) is one of the popular cluster-based structures, which has been widely proposed in wireless sensor networks. LEACH uses a TDMA based MAC protocol, and in order to maintain a balanced energy consumption, suggests that each node probabilistically become a cluster head. To reduce the energy consumption and to avoid the strict synchronization requirements of TDMA, we first apply a sleep-wakeup based decentralized MAC protocol to LEACH, then we present an analytic framework for obtaining the optimal probability with which a node becomes a cluster head in order to minimize the network's energy consumption. The analysis is first presented for small networks, under the assumption of identical expected distance of all cluster heads from the sink. Then the analysis is extended for large networks to consider the case when the distances of various sections of the network from the sink may be different, since nodes further away have to spend greater energy in order to reach the sink. Our simulation results show that using this optimal probability results in much more efficient energy consumption and compared with the current LEACH, our proposal consumes significantly less power.

The most popular strategies for dealing with packet collisions at the medium access control (MAC) layer in distributed wireless networks use a combination of carrier sensing and collision avoidance. When the collision avoidance strategy fails, such schemes cannot detect collisions and corrupted data frames are still transmitted in their entirety, thereby wasting the channel bandwidth and significantly reducing the network throughput. To address this problem, this paper proposes a new wireless MAC protocol capable of collision detection. The basic idea of the proposed protocol is the use of pulses in an out-of-band control channel for exploring channel condition and medium reservation and achieving both collision avoidance and collision detection. The performance of the proposed MAC protocol has been investigated using extensive analysis and simulations. Our results show that, as compared with existing MAC protocols, the proposed protocol has significant performance gains in terms of node throughput. Additionally, the proposed protocol is fully distributed and requires no time synchronization among nodes.

In this paper, we present an analytic model for evaluating the queueing delays at nodes using the IEEE 802.11 point coordination function (PCF) MAC for real time, delay sensitive traffic. We develop a queueing model to obtain closed form expressions for the expected delay at each node which accounts for arbitrary (but fixed) packet sizes, polling rates, channel rates and the order in which the nodes are polled. The model is then further extended to account for the delays when the nodes use power management, and for cases when not all nodes are served in a frame. Our analytical results are verified through simulations. The model is also extended to evaluate the number of nodes that can be supported by a base station while satisfying an arbitrary delay requirement at all nodes and can be used as a mechanism for admission control by the base station

In this paper, we investigate the spatial reuse and saturation throughput of static ad-hoc networks with unbiased medium access control (MAC) protocols. Under the stochastic assumptions of our model, we obtain the upper bound on the equivalent saturation throughput of such MAC protocols as a function of node density. We also obtain the scaling properties of the spatial reuse and saturation throughput.

A number of events such as hurricanes, earthquakes, power outages can cause large-scale failures in the Internet. These in turn cause anomalies in the interdomain routing process. The policy-based nature of border gateway protocol (BGP) further aggravates the effect of these anomalies causing severe, long lasting route fluctuations. In this work we propose an architecture for anomaly detection that can be implemented on individual routers. We use statistical pattern recognition techniques for extracting meaningful features from the BGP update message data. A time-series segmentation algorithm is then carried out on the feature traces to detect the onset of an instability event The performance of the proposed algorithm is evaluated using real Internet trace data. We show that instabilities triggered by events like router mis-configurations, infrastructure failures and worm attacks can be detected with a false alarm rate as low as 0.0083 alarms per hour. We also show that our learning based mechanism is highly robust as compared to methods like exponentially weighted moving average (EWMA) based detection.

Medium access control (MAC) protocols for wireless packet networks usually need to be distributed for flexibility and robustness. In a distributed way, however, collision detection becomes difficult and collided packets are usually still fully transmitted with existing wireless MAC protocols, which wastes the precious medium resource of the network. This paper proposes a new MAC protocol that realizes effective and efficient collision detection in wireless packet networks by the use of pulses of random-length pauses. Our comprehensive simulation results show the capability of the new protocol for significantly improving the throughput of future wireless packet networks.

In this paper we propose a distributed, mobility transparent broadcast (DMTB) protocol to achieve efficient and effective broadcast in mobile ad-hoc networks. The protocol is fully distributed and highly adaptive to node mobility. It does not demand any neighborhood information and incurs little overhead. On one hand, the cross-layer design approach helps achieve effective broadcast with higher efficiency and alleviated interference; on the other hand, the proposed protocol achieves network energy balance by randomly rotating the set of relay nodes in different broadcast events even when the network topology stays unchanged. The protocol's performance is proved to be within a constant of the optimum. Detailed analysis regarding the broadcast interference, node density and protocol overhead is presented. DMTB is not only efficient, but also robust against node failures and scalable with the node density or the network area size.

In a wireless ad hoc network where multi-hop traffic dominates the network, spatial reuse has an enormous impact on the network performance in terms of end-to-end throughput and delay characteristics. In this paper, we investigate the MAC coordination of persistent flows in 802.11 ad hoc networks and point out that the aggressive behavior of 802.11 MAC can throttle the spatial reuse and reduce bandwidth efficiency. We thus propose an adaptive layer-2 pacing scheme fully compatible with 802.11 MAC using explicit MAC feedback to balance the transmissions on adjacent nodes. By promoting MAC coordination, our scheme can assist the MAC to operate around its saturation state while minimizing resource contention. Experiment results demonstrate that our scheme significantly outperforms the original 802.11 MAC by boosting the throughput while still maintaining latency at a low level.

Polymorphic worms can change their byte sequence as they replicate and propagate, thwarting the traditional signature analysis techniques used by many intrusion detection systems (IDSes). As the incidence of such worms becomes more frequent, it is important to understand their behavior and interaction with the IDSes in order to develop effective strategies to control their propagation. In this paper, we propose a model based on coevolution of biological quasi-species to characterize the propagation of polymorphic worms and the effects of dynamic IDSes which improve their detection capability with time. The model is used to derive the maximum allowable response time of the IDS in order to contain the worm and the optimal mutation rate the worm should use in order to escape an IDS with a given response time. The observations from the model are validated using simulations with the ADMmutate polymorphic engine.

Abstract — This paper addresses the data acquisition problem in sensor networks using mobile sinks. Sensor nodes ’ low computational capabilities and limited energy motivate our design of a swarm intelligence based, energy aware protocol, SIMPLE, to route data to the mobile destination via the shortest paths. Using a swarm agent technique to integrate nodes ’ residual energy as a metric for the shortest path selection, SIMPLE maximizes the network’s lifetime by evenly balancing residual energy across the network and minimizing the protocol overhead. The protocol’s resilience against node failures is guaranteed by the multiple path technique. It scales well with both the network size and multiple sinks. Simulation results are presented to observe and verify SIMPLE’s performance and robustness. Compared with existing algorithms, SIMPLE is shown to have superior performance. A general tradeoff model is presented to evaluate the performance tradeoffs associated with different protocol parameters. Index Terms — Swarm intelligence, data acquisition, mobilesink, sensor network, energy awareness

A key emerging and popular communication paradigm, primarily employed for information dissemination, is peer-to-peer (P2P) networking. In this paper, we model the spread of malware in decentralized, Gnutella type of peer-to-peer networks. Our study reveals that the existing bound on the spectral radius governing the possibility of an epidemic outbreak needs to be revised in the context of a P2P network. We formulate an analytical model that emulates the mechanics of a decentralized Gnutella type of peer network and study the spread of malware on such networks. We show analytically, that a framework which does not incorporate the behavioral characteristics of peers ends up over estimating the epidemic threshold metric, R0. This in turn results in false positives, an undesirable feature. We also characterize the conditions under which the network may reach a malware free equilibrium and validate our theoretical results with numerical simulations

We analyze the delay performance of multi-channel MAC in point-to-multipoint wireless networks. We focus on contention based operation of nodes in such networks using a multi-channel MAC protocol adapted from Jungmin So et al., (2004). Our analysis can be extended to IEEE 802.11 DCF in PMP mode of operation and contention based IEEE 802.16 based networks. Using a slotted-time model, we derive expressions for the service time distribution of the packets, and approximate expressions for the queuing delay under Poisson traffic, when the contention window is of fixed size. We extend this analysis to incorporate exponential backoff of the contention window size. We conduct simulations for the multichannel MAC protocol in NS-2, and note that our analytical results match well with simulation results for moderate to high values of traffic intensity. Our analysis enables us to capture the imp act of various system parameters on queuing delay

In this paper, we present a delay analysis of IEEE 802.16 based broadband wireless access networks, a promising technology capable of supporting both fixed and fully mobile operations while offering integrated voice, video and data services. Our work develops analytic models to evaluate the performance of IEEE 802.16 based networks in terms of their latencies as a function of various scheduling policies. The model also allows us to explore the impact of various system parameters like the sub-frame lengths on the performance and thereby aid in system design. The results of our model can also be used for providing probabilistic quality of service guarantees and determining the number of nodes that can be accommodated while satisfying a given delay constraint. The analytic models are verified using extensive simulations

In this paper, we present an analytic model for evaluating the queueing delays at nodes using the IEEE 802.11 point coordination function (PCF) MAC for real time, delay sensitive traffic. Our work extends existing models by accounting for the power management mode where nodes may switch to the power save mode in order to conserve energy. We develop a queueing model to obtain closed form expressions for the expected delay at each node which accounts for arbitrary packet sizes, polling rates, channel rates and the order in which nodes are polled. Our analytical results are verified through simulations.

This paper presents an analytic framework to evaluate the performance of peer to peer (P2P) networks. Using the time to download or replicate an arbitrary file as the metric, we present a model which accurately captures the impact of various network and peer level characteristics on the performance of a P2P network. We propose a queueing model which evaluates the delays in the routers using a single class open queueing network and the peers as M/G/1/K processor sharing queues. The framework takes into account the underlying physical network topology and arbitrary file sizes, the search time, load distribution at peers and number of concurrent downloads allowed by a peer. The model has been validated using extensive simulations with campus level, power law AS level and ISP level topologies. The paper also describes the impact of various parameters associated with the network and peers including external traffic rates, service variability, file popularity etc. on the download times. We also show that in scenarios with multi-part downloads from different peers, a rate proportional allocation strategy minimizes the download times.

With the increased popularity of multimedia services on the Internet, efficient video multicast strategies that can scale easily are of critical importance. This paper addresses the issue of video multicast loss recovery and presents an efficient and scalable scheme: Active Injection Recovery (AIR). The proposed scheme has three distinguishing features: active injection of repair packets into loss regions, on-demand construction of loss-recovery structures, and unique rate control over repair traffic. All of these features can save considerable network resources in a large-scale video multicast session. In addition, the proposed scheme simultaneously meets the three well-known requirements for efficiency and scalability in multicast loss recovery: request suppression, local recovery, and retransmission scoping. Another important feature of the proposed scheme is its low recovery latency, which is essential for video multicast. Our results show that the proposed scheme achieves significantly better overall performance as compared to existing multicast loss recovery schemes.

Fast convergence of BGP (border gateway protocol) routes, coupled with reduced message complexity, form one of the key factors determining the stability and performance of inter-domain routing in the Internet. The paper characterizes the impact of topology and the message handling procedure of BGP on its convergence time. For BGP router networks characterized by random graphs, we obtain analytic expressions to evaluate the convergence times in terms of the number of minimum route advertisement interval (MRAI) timer rounds. We then isolate artifacts in the BGP message handling procedure which lead to redundant MRAI timer instances and propose minor, but effective, modifications which significantly reduce the convergence times. Simulation results show that the proposed changes can successfully eliminate all redundant instantiations of the MRAI timer, even in the worst case convergence scenarios resulting from route withdrawals.

The increasing incidence of worm attacks in the Internet and the resulting instabilities in the global routing properties of the border gateway protocol (BGP) routers pose a serious threat to the connectivity and the ability of the Internet to deliver data correctly. In this paper we propose a mechanism to detect/predict the onset of such instabilities which can then enable the timely execution of preventive strategies in order to minimize the damage caused by the worm. Our technique is based on online statistical methods relying on sequential change-point and persistence filter based detection algorithms. Our technique is validated using a year's worth of real traces collected from BGP routers in the Internet that we use to detect/predict the global routing instabilities corresponding to the Code Red II, Nimda and SQL Slammer worms.

In this paper we address the issue of improving the spatial reuse in virtual carrier sensing (VCS) mechanisms for wireless networks. The paper examines in detail the channel reservation mechanisms of IEEE 802.11 VCS and shows that its spatial reuse is sub-optimal in a number of scenarios. We also show that the area that should be reserved by the VCS depends on the distance between the transmitter and receiver. We then present a novel VCS scheme that optimizes spatial reuse by incorporating this distance information in the decision making process for the channel reservation. Unlike existing proposals for improving spatial reuse, our scheme does not rely on special hardware design such as directional antennas, power adaptable or dual-channel devices etc., and is thus easily implementable. Simulation results quantify and demonstrate the substantial performance improvements obtained by the proposed scheme.

This paper presents an analytic model for evaluating the MAC layer queueing delays at wireless nodes using the distributed coordination function of IEEE 802.11 MAC specifications. Our model is valid for finite loads and can account for arbitrary arrival patterns, packet size distributions and number of nodes. Each node is modeled as a discrete time G/G/1 queue and we obtain closed form expressions for the delay and queue length characteristics at each node. We derive the service time distribution for the packets at each node while accounting for a number of factors including the channel access delay due to the shared medium, impact of packet collisions, the resulting backoffs as well as the packet size distribution. Our analytical results are verified through extensive simulations and are more accurate than existing models.

We present an analytic model for evaluating the queueing delays at nodes in an IEEE 802.11 MAC based wireless network. The model can account for arbitrary arrival patterns, packet size distributions and number of nodes. Our model gives closed form expressions for obtaining the delay and queue length characteristics. We model each node as a discrete time G/G/1 queue and derive the service time distribution while accounting for a number of factors including the channel access delay due to the shared medium, impact of packet collisions, the resulting backoffs as well as the packet size distribution. The model is also extended for ongoing proposals under consideration for 802.11e wherein a number of packets may be transmitted in a burst once the channel is accessed. Our analytical results are verified through extensive simulations. The results of our model can also be used for providing probabilistic quality of service guarantees and determining the number of nodes that can be accommodated while satisfying a given delay constraint.

Static broadcast tree protocols have been proposed in literature to optimize the querying procedure in sensor networks. In this paper we address the issue of how to mitigate the unevenness of energy distribution and its undesirable effects like reduced network lifetime and loss of connectivity in a sensor network that are caused by static broadcast trees. We propose a "dynamic query-tree energy balancing" (DQEB) protocol to dynamically adjust the tree structure and minimize the overall broadcast cost. The proposed algorithm scales well, is distributed and does not need any global information. Locally, the broadcast power consumption is minimized while globally, the broadcast load and power distribution are balanced across the whole sensor network. Our simulation results verify that the DQEB protocol achieves significantly better balance in the battery power distribution and extends the network's lifetime considerably.

It is well known that long and variable link delays, link errors, and handoffs in satellite environments seriously interfere with transmission control protocol's (TCP's) congestion control mechanisms. These channel characteristics also adversely affect existing multilayer multicast congestion control schemes when they are used in satellite environments. In addition, these schemes still have problems with fairly sharing bandwidth with TCP flows, controlling the overhead of frequent grafting and pruning, and handling misbehaving receivers. In this paper, we present a new multilayer multicast congestion control scheme that is suitable for satellite environments and overcomes most of the disadvantages of existing schemes. Our scheme is not affected by the long and variable delays of satellite links. Link errors also do not decrease the performance of our scheme. Further, our scheme has very limited control overhead. In addition to these advantages specific to satellite environments, our scheme achieves good fairness in sharing bandwidth with TCP sessions and is not sensitive to misbehaving receivers.

This paper presents a multi-rate multicast congestion control scheme for mobile ad-hoc networks (MANETs). Not only does the proposed scheme overcome the disadvantages of existing multicast congestion control protocols which prevent them from being used in MANETs, but it also achieves good performance in other aspects such as fairness with TCP, robustness against misbehaving receivers, and traffic stability. Besides achieving the above advantages, the proposed scheme does not impose any significant changes on the queuing, scheduling or forwarding policies of existing networks.

In this paper, we evaluate and suggest methods to improve the performance of IEEE 802.11 based ad hoc networks from the perspective of spatial reuse. Since 802.11 employs virtual carrier sensing to reserve the medium prior to a packet transmission, the relative size of the spatial region it reserves for the impending traffic significantly affects the overall network performance. We show that the space reserved by 802.11 for a successful transmission is far from optimal and depending on the one hop distances between the sender and the receiver. We study three scenarios with very different spatial reuse characteristics. We also introduce a new quantitative measure, the spatial reuse index, to evaluate the efficiency of the medium reservation accomplished by 802.11 virtual carrier sensing. We also propose an improved virtual carrier sensing mechanism for wireless LAN scenarios and using analysis and simulation results, show that it can significantly increase the spatial reuse and network throughput.

Accurate positioning mechanisms are important in large scale sensor networks to achieve a number of functionalities like location aware routing, efficient coordination of resources and other application specific requirements. This paper proposes a distributed and scalable GPS free positioning algorithm for wireless sensor network. This approach is an effort in the direction of finding a solution to the positioning problem, which minimizes the number of messages exchanged and the coordinate setup time. We use a clustering based approach for the coordinate formation wherein a small subset of the nodes can successfully establish the coordinate system for the whole network. We also compare the performance of this system against existing mechanisms and show that our system scales linearly as the number of nodes in the network increases in contrast to the exponential increase in current mechanism. Additionally, out mechanism takes considerably lower convergence times. The proposed mechanism takes considerably lower convergence times. The proposed mechanism is scalable, distributed and able to support the ad hoc deployment of large scale sensor networks quickly and efficiently.

It is well known that request suppression, local recovery and retransmission scoping are the three crucial elements for scalability and efficiency in multicast loss recovery. None of existing multicast loss recovery schemes can simultaneously have good performance in all the three aspects without introducing significant overhead. The scheme proposed in this paper approaches the multicast loss recovery issue from a new perspective and achieves good performance in all the three aspects with very limited overhead. Our analysis shows that the proposed scheme has a significantly better overall performance as compared to existing schemes.

IEEE 802.11 medium access control (MAC) is gaining widespread popularity as a layer-2 protocol for wireless local-area networks. While efforts have been made previously to evaluate the performance of various protocols in wireless networks and to evaluate the capacity of wireless networks, very little is understood or known about the traffic characteristics of wireless networks. In this paper, we address this issue and first develop an analytic model to characterize the interarrival time distribution of traffic in wireless networks with fixed base stations or ad hoc networks using the 802.11 MAC. Our analytic model and supporting simulation results show that the 802.11 MAC can induce pacing in the traffic and the resulting interarrival times are best characterized by a multimodal distribution. This is a sharp departure from behavior in wired networks and can significantly alter the second order characteristics of the traffic, which forms the second part of our study. Through simulations, we show that while the traffic patterns at the individual sources are more consistent with long-range dependence and self-similarity, in contrast to wired networks, the aggregate traffic is not self-similar. The aggregate traffic is better classified as a multifractal process and we conjecture that the various peaks of the multimodal interarrival time distribution have a direct contribution to the differing scaling exponents at various timescales.

Continuing the process of improvements made to TCP through the addition of new algorithms in Tahoe and Reno, TCP SACK aims to provide robustness to TCP in the presence of multiple losses from the same window. In this paper we present analytic models to estimate the latency and steady-state throughput of TCP Tahoe, Reno, and SACK and validate our models using both simulations and TCP traces collected from the Internet. In addition to being the first models for the latency of finite Tahoe and SACK flows, our model for the latency of TCP Reno gives a more accurate estimation of the transfer times than existing models. The improved accuracy is partly due to a more accurate modeling of the timeouts, evolution of cwnd during slow start and the delayed ACK timer. Our models also show that, under the losses introduced by the droptail queues which dominate most routers in the Internet, current implementations of SACK can fail to provide adequate protection against timeouts and a loss of roughly more than half the packets in a round will lead to timeouts. We also show that with independent losses SACK performs better than Tahoe and Reno and, as losses become correlated, Tahoe can outperform both Reno and SACK.

While it is well known that second order scaling in network traffic can lead to larger queueing delays, higher drop rates and extended periods of congestion, reducing the scaling exponents has remained an open problem. In this paper we evaluate some techniques to reduce the degree of scaling in TCP traffic, specifically by reducing two related causes: (1) timeouts and exponential backoffs; and (2) burstiness and ACK compression. We propose a simple modification to the RED algorithm, and show that it can lead to significant reductions in both multi and mono fractal properties of TCP traffic as compared to the currently implemented active and passive buffer management policies. We then evaluate TCP pacing and show that it too can reduce the multi and mono fractal scaling of traffic. We also show that though our techniques are aimed at small time-scale TCP related causes of scaling, they are also effective in reducing the degree of self-similarity in traffic even when application and user level causes are also present, as long as TCP is used as the underlying transport protocol.

This paper presents an analytic model for characterizing the traffic in wireless networks using IEEE 802.11 as the MAC protocol. The results of this paper are aimed at filling the existing void created by the absence of any accurate models or understanding of wireless traffic, critical for effective performance evaluation. Our results show that the behavior of wireless traffic can vary significantly from the characteristics of traffic in wired networks. We show that the operating mechanism of 802.11 MAC leads to "pacing" in the wireless traffic. Additionally, the interarrival times are best characterized by a multimodal distribution, In sharp contrast to the models used for for wired networks. The analytic model has been verified through extensive simulations and is applicable to both ad hoc and infrastructure based wireless networks.

In this paper we present analytic models to estimate the latency and steady-state throughput of TCP Tahoe, Reno and SACK and validate our models using both simulations and TCP traces collected from the Internet. We also conduct a study comparing the performance of these versions of TCP under different loss scenarios. In addition to being the first models for the latency of finite Tahoe and SACK flows, our model for the latency of TCP Reno gives a more accurate estimation of the transfer times than existing models. Our models show that under the losses introduced by the droptail queues which dominate most routers in the Internet, current implementations of SACK fail to provide adequate protection against timeouts and a loss of roughly more than half the packets in a round will lead to timeouts. We also show that with independent losses, SACK performs better than Tahoe and Reno and as losses become correlated, Tahoe can outperform both Reno and SACK.

The complexity and dynamics of the Internet is driving the demand for scalable and effective network control. This paper proposes a collaborative on-line simulation architecture to provide pro-active and automated control functions for networks. The general model includes autonomous on-line simulators which continuously monitor/model the network conditions and execute a search in the parameter state space for better settings of the protocol parameters. The protocol parameters are then tuned by the on-line simulation system. We describe the building blocks of this architecture and investigate the implementation challenges in the areas of network modeling, on-line simulation and parameter search. We also discuss the applicability of this system and present the simulation and test results of a preliminary implementation.

previous studies have shown that TCP traffic displays strong multifractal scaling. However a physical explanation of why such a behavior occurs is still elusive. We propose a cascade model that is based on the retransmission and congestion avoidance mechanisms of TCP. At the same time, it relates to the physical, tree-like organization of networks. This model allows one to relate the most salient multifractal features with basic traffic parameters as the round trip time (RTT) and the loss probability. Numerical experiments confirm that such a parsimonious model is able to give a satisfactory explanation for a number of features pertaining to multifractality, including the range of scales where it is observed. We believe these results open the way to a more profound understanding of the small time scale properties of TCP traffic.

We consider crossbar switches for switching variable length packets. Analysis of such switches is important in the context of IP switches where the packet interarrival times and packet lengths are drawn from continuous distributions. Assuming a single stage M/spl times/N switch we obtain a very general throughput delay model for Poisson packet arrivals and exponential service times. We then analyze an M/spl times/N switch for self similar packet arrivals and exponential packet lengths. An MMPP (Markov modulated Poisson process) based self similar arrival process model corresponding to the arrival rate, the autocorrelation, the Hurst parameter and the time scales over which burstiness exists in the input process is first obtained using results from Andersen and Nielsen (1998). We then use queuing theory available for MMPP/G/1 queues to model the switch performance for self similar packet arrivals. The results from the analytical model are compared against those from a simulation model that is driven by traces that are statistically similar to the Bellcore traces. We also analyse the effect of link multiplicities (speedup) to the output and asymmetries in the input traffic.

Space-based multicast switches use copy networks to generate the copies requested by the input packets. In this paper our interest is in the multicast switch proposed by Lee (1988). The order in which the copy requests of the input ports are served is determined by the copy scheduling policy and this plays a major part in defining the performance characteristics of a multicast switch. In any slot, the sum of the number of copies requested by the active inputs of the copy network may exceed the number of output ports and some of the copy requests may need to be dropped or buffered. We first propose an exact model to calculate the overflow probabilities in an unbuffered Lee's copy network. Our exact results improve upon the Chernoff bounds on the overflow probability given by Lee by a factor of more than 10. Next, we consider buffered inputs and propose queueing models for the copy network for three scheduling policies: cyclic service of the input ports with and without fanout splitting of copy requests and acyclic service without fanout splitting. These queueing models obtain the average delay experienced by the copy requests. We also obtain the sustainable throughput of a copy network, the maximum load that can be applied to all the input ports without causing an unstable queue at any of the inputs, for the scheduling policies mentioned above.