Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance

We show that it is possible to achieve information theoretic location privacy for secondary users (SUs) in database-driven cognitive radio networks (CRNs) with an end-to-end delay less than a second, which is significantly better than that of the existing alternatives offering only a computational privacy. This is achieved based on a keen observation that, by the requirement of Federal Communications Commission (FCC), all certified spectrum databases synchronize their records. Hence, the same copy of spectrum database is available through multiple (distinct) providers. We harness the synergy between multi-server private information retrieval (PIR) and database- driven CRN architecture to offer an optimal level of privacy with high efficiency by exploiting this observation. We demonstrated, analytically and experimentally with deployments on actual cloud systems that, our adaptations of multi-server PIR outperform that of the (currently) fastest single-server PIR by a magnitude of times with information theoretic security, collusion resiliency, and fault-tolerance features. Our analysis indicates that multi-server PIR is an ideal cryptographic tool to provide location privacy in database-driven CRNs, in which the requirement of replicated databases is a natural part of the system architecture, and therefore SUs can enjoy all advantages of multi-server PIR without any additional architectural and deployment costs.Comment: 10 pages, double colum

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

Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Location Privacy-Preserving Strategies for Secondary Spectrum Use

The scarcity of wireless spectrum resources and the overwhelming demand for wireless broadband resources have prompted industry, government agencies and academia within the wireless communities to develop and come up with effective solutions that can make additional spectrum available for broadband data. As part of these ongoing efforts, cognitive radio networks (CRNs) have emerged as an essential technology for enabling and promoting dynamic spectrum access and sharing, a paradigm primarily aimed at addressing the spectrum scarcity and shortage challenges by permitting and enabling unlicensed or secondary users (SUs) to freely search, locate and exploit unused licensed spectrum opportunities. Despite their great potentials for improving spectrum utilization efficiency and for addressing the spectrum shortage problem, CRNs suffer from serious location privacy issues, which essentially tend to disclose the location information of the SUs to other system entities during their usage of these open spectrum opportunities. Knowing that their whereabouts may be exposed, SUs can be discouraged from joining and participating in the CRNs, potentially hindering the adoption and deployment of this technology. In this thesis, we propose frameworks that are suitable for CRNs, but also preserve the location privacy information of these SU s. More specifically, 1. We propose location privacy-preserving protocols that protect the location privacy of SUs in cooperative sensing-based CRNs while allowing the SUs to perform their spectrum sensing tasks reliably and effectively. Our proposed protocols allow also the detection of malicious user activities through the adoption of reputation mechanisms. 2. We propose location privacy-preserving approaches that provide information-theoretic privacy to SU s’ location in database-driven CRNs through the exploitation of the structured nature of spectrum databases and the fact that database-driven CRNs, by design, rely on multiple spectrum databases. 3. We propose a trustworthy framework for new generation of spectrum access systems in the 3.5 GHz band that not only protects SUs’ privacy, but also ensures that they comply with the unique system requirements, while allowing the detection of misbehaving users

Over-the-Air Integrated Sensing, Communication, and Computation in IoT Networks

To facilitate the development of Internet of Things (IoT) services, tremendous IoT devices are deployed in the wireless network to collect and pass data to the server for further processing. Aiming at improving the data sensing and delivering efficiency, the integrated sensing and communication (ISAC) technique has been proposed to design dual-functional signals for both radar sensing and data communication. To accelerate the data processing, the function computation via signal transmission is enabled by over-the-air computation (AirComp), which is based on the analog-wave addition property in a multi-access channel. As a natural combination, the emerging technology namely over-the-air integrated sensing, communication, and computation (Air-ISCC) adopts both the promising performances of ISAC and AirComp to improve the spectrum efficiency and reduce latency by enabling simultaneous sensing, communication, and computation. In this article, we provide a promptly overview of Air-ISCC by introducing the fundamentals, discussing the advanced techniques, and identifying the applications

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