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

Security in Dynamic Spectrum Access Systems: A Survey

Dynamic Spectrum Access (DSA) systems are being developed to improve spectrum utilization. Most of the research on DSA systems assumes that the participants involved are honest, cooperative, and that no malicious adversaries will attack or exploit the network. Some recent research efforts have focused on studying security issues in cognitive radios but there are still significant security challenges in the implementation of DSA systems that have not been addressed. In this paper we focus on security issues in DSA. We identify various attacks (e.g., DoS attacks, system penetration, repudiation, spoofing, authorization violation, malware infection, data modification, etc.) and suggest various approaches to address them. We show that significant security issues exist that should be addressed by the research community if DSA is to find its way into production systems. We also show that, in many cases, existing approaches to securing IT systems can be applied to DSA and identify other DSA specific security challenges where additional research will be required

Spectrum Sensing Security in Cognitive Radio Networks

This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks