A Mixed-Integer Programming Approach for Jammer Placement Problems for Flow-Jamming Attacks on Wireless Communication Networks

In this dissertation, we study an important problem of security in wireless networks. We study different attacks and defense strategies in general and more specifically jamming attacks. We begin the dissertation by providing a tutorial introducing the operations research community to the various types of attacks and defense strategies in wireless networks. In this tutorial, we give examples of mathematical programming models to model jamming attacks and defense against jamming attacks in wireless networks. Later we provide a comprehensive taxonomic classification of the various types of jamming attacks and defense against jamming attacks. The classification scheme will provide a one stop location for future researchers on various jamming attack and defense strategies studied in literature. This classification scheme also highlights the areas of research in jamming attack and defense against jamming attacks which have received less attention and could be a good area of focus for future research. In the next chapter, we provide a bi-level mathematical programming model to study jamming attack and defense strategy. We solve this using a game-theoretic approach and also study the impact of power level, location of jamming device, and the number of transmission channels available to transmit data on the attack and defense against jamming attacks. We show that by increasing the number of jamming devices the throughput of the network drops by at least 7%. Finally we study a special type of jamming attack, flow-jamming attack. We provide a mathematical programming model to solve the location of jamming devices to increase the impact of flow-jamming attacks on wireless networks. We provide a Benders decomposition algorithm along with some acceleration techniques to solve large problem instances in reasonable amount of time. We draw some insights about the impact of power, location and size of the network on the impact of flow-jamming attacks in wireless networks

A Mixed-Integer Programming Approach for Jammer Placement Problems for Flow-Jamming Attacks on Wireless Communication Networks

In this dissertation, we study an important problem of security in wireless networks. We study different attacks and defense strategies in general and more specifically jamming attacks. We begin the dissertation by providing a tutorial introducing the operations research community to the various types of attacks and defense strategies in wireless networks. In this tutorial, we give examples of mathematical programming models to model jamming attacks and defense against jamming attacks in wireless networks. Later we provide a comprehensive taxonomic classification of the various types of jamming attacks and defense against jamming attacks. The classification scheme will provide a one stop location for future researchers on various jamming attack and defense strategies studied in literature. This classification scheme also highlights the areas of research in jamming attack and defense against jamming attacks which have received less attention and could be a good area of focus for future research. In the next chapter, we provide a bi-level mathematical programming model to study jamming attack and defense strategy. We solve this using a game-theoretic approach and also study the impact of power level, location of jamming device, and the number of transmission channels available to transmit data on the attack and defense against jamming attacks. We show that by increasing the number of jamming devices the throughput of the network drops by at least 7%. Finally we study a special type of jamming attack, flow-jamming attack. We provide a mathematical programming model to solve the location of jamming devices to increase the impact of flow-jamming attacks on wireless networks. We provide a Benders decomposition algorithm along with some acceleration techniques to solve large problem instances in reasonable amount of time. We draw some insights about the impact of power, location and size of the network on the impact of flow-jamming attacks in wireless networks

Learning-based Decision Making in Wireless Communications

Fueled by emerging applications and exponential increase in data traffic, wireless networks have recently grown significantly and become more complex. In such large-scale complex wireless networks, it is challenging and, oftentimes, infeasible for conventional optimization methods to quickly solve critical decision-making problems. With this motivation, in this thesis, machine learning methods are developed and utilized for obtaining optimal/near-optimal solutions for timely decision making in wireless networks. Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. In this context, we in the first part of the thesis study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. Initially, we develop a learning-based caching policy for a single base station aiming at maximizing the long-term cache hit rate. Then, we extend this study to a wireless communication network with multiple edge nodes. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. Next, with the purpose of making efficient use of limited spectral resources, we develop a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework\u27s tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. Following the analysis of the proposed learning-based dynamic multichannel access policy, we consider adversarial attacks on it. In particular, we propose two adversarial policies, one based on feed-forward neural networks and the other based on deep reinforcement learning policies. Both attack strategies aim at minimizing the accuracy of a deep reinforcement learning based dynamic channel access agent, and we demonstrate and compare their performances. Next, anomaly detection as an active hypothesis test problem is studied. Specifically, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. Separately, we also regard the detection of threshold crossing as an anomaly detection problem, and analyze it via hierarchical generative adversarial networks (GANs). In the final part of the thesis, to address state estimation and detection problems in the presence of noisy sensor observations and probing costs, we develop a soft actor-critic deep reinforcement learning framework. Moreover, considering Byzantine attacks, we design a GAN-based framework to identify the Byzantine sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total probing cost needed for detection

n-Tier Modelling of Robust Key management for Secure Data Aggregation in Wireless Sensor Network

Security problems in Wireless Sensor Network (WSN) have been researched from more than a decade. There are various security approaches being evolving towards resisting various forms of attack using different methodologies. After reviewing the existing security approaches, it can be concluded that such security approaches are highly attack-specific and doesnt address various associated issues in WSN. It is essential for security approach to be computationally lightweight. Therefore, this paper presents a novel analytical modelling that is based on n-tier approach with a target to generate an optimized secret key that could ensure higher degree of security during the process of data aggregation in WSN. The study outcome shows that proposed system is computationally lightweight with good performance on reduced delay and reduced energy consumption. It also exhibits enhanced response time and good data delivery performance to balance the need of security and data forwarding performance in WSN

A Survey on the Communication Protocols and Security in Cognitive Radio Networks

A cognitive radio (CR) is a radio that can change its transmission parameters based on the perceived availability of the spectrum bands in its operating environment. CRs support dynamic spectrum access and can facilitate a secondary unlicensed user to efficiently utilize the available underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose radios need not be CR-enabled. Most of the active research conducted in the area of CRNs has been so far focused on spectrum sensing, allocation and sharing. There is no comprehensive review paper available on the strategies for medium access control (MAC), routing and transport layer protocols, and the appropriate representative solutions for CRNs. In this paper, we provide an exhaustive analysis of the various techniques/mechanisms that have been proposed in the literature for communication protocols (at the MAC, routing and transport layers), in the context of a CRN, as well as discuss in detail several security attacks that could be launched on CRNs and the countermeasure solutions that have been proposed to avoid or mitigate them. This paper would serve as a good comprehensive review and analysis of the strategies for MAC, routing and transport protocols and security issues for CRNs as well as would lay a strong foundation for someone to further delve onto any particular aspect in greater depth

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Jamming Attack Resiliency and Performance Analysis of Cognitive Radio Communication Networks

Cognitive radio technology emerges as a promising solution for overcoming shortage and inefficient use of spectrum resources. In cognitive radio networks, secondary users, which are users equipped with cognitive radios, can opportunistically access spectrum assigned to primary users, the spectrum license holders. Although it improves spectrum utilization efficiency, this opportunistic spectrum access incurs undesired delays that can degrade the quality of service (QoS) of delay-sensitive applications substantially. It is therefore important to understand, model, and characterize these delays, as well as their dependency on primary user behaviors. Moreover, the lack of access priority leads to significant performance degradation when the network is under jamming attacks. It turns out that addressing jamming attacks while maintaining a desired QoS is very challenging. In this thesis, we characterize the properties of the random process that describes the availability of the opportunistic resources, and analytically model and analyze cognitive network average delays. Furthermore, we propose and study new techniques that mitigate jamming attacks in mobile cognitive radio networks. More specifically, this thesis consists of the following three complimentary frameworks:Bechir Hamdaoui1. Stochastic Resource Availability Modeling and Delay Analysis. In this framework, we define and characterize the properties of the random process that describes the availability of the opportunistic network resources. We apply the mean residual service time concept to derive an analytical solution for the cognitive network queueing delay. We model the service mechanism, and determine the manner in which it depends on spectrum availability. We show that the delay becomes unbounded if spectrum dynamics are not carefully considered in network design.2. Mitigating Jamming through Pseudorandom Time Hopping. In this framework, we propose and evaluate jamming countermeasure approaches for mobile cognitive users. We propose two time-based techniques which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on spectrum handoff to countermeasure jamming. In these two techniques, we allocate data over time based on cryptographic and estimation methods. We derive analytical expressions of the jamming, switching and error probabilities. Our findings show that our proposed technique outperforms other existing frequency-based techniques.3. Optimally Controlled Time-Hopping Anti-Jamming Technique. In this framework, we propose a jamming and environment aware resource allocation method for mobile cognitive users. We propose to mitigate jamming based on an optimal allocation of data over time. In addition, we optimally control network mobility to meet a desired QoS. Our findings show that our proposed technique achieves better QoS than those achieved by existing cryptographic methods while not compromising jamming resiliency.Keywords: Performance Analysis, Delay Analysis, Cognitive Radio Networks, Jamming Attack Resilienc

A survey of symbiotic radio: Methodologies, applications, and future directions

The sixth generation (6G) wireless technology aims to achieve global connectivity with environmentally sustainable networks to improve the overall quality of life. The driving force behind these networks is the rapid evolution of the Internet of Things (IoT), which has led to a proliferation of wireless applications across various domains through the massive deployment of IoT devices. The major challenge is to support these devices with limited radio spectrum and energy-efficient communication. Symbiotic radio (SRad) technology is a promising solution that enables cooperative resource-sharing among radio systems through symbiotic relationships. By fostering mutualistic and competitive resource sharing, SRad technology enables the achievement of both common and individual objectives among the different systems. It is a cutting-edge approach that allows for the creation of new paradigms and efficient resource sharing and management. In this article, we present a detailed survey of SRad with the goal of offering valuable insights for future research and applications. To achieve this, we delve into the fundamental concepts of SRad technology, including radio symbiosis and its symbiotic relationships for coexistence and resource sharing among radio systems. We then review the state-of-the-art methodologies in-depth and introduce potential applications. Finally, we identify and discuss the open challenges and future research directions in this field