Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs

Abstract

Cognitive radio networks (CRNs) have been recognized as a promising technology that allows secondary users (SUs) extensively explore spectrum resource usage efficiency, while not introducing interference to licensed users. Due to the unregulated wireless network environment, CRNs are susceptible to various malicious entities. Thus, it is critical to detect anomalies in the first place. However, from the perspective of intrinsic features of CRNs, there is hardly in existence of an universal applicable anomaly detection scheme. Singular Spectrum Analysis (SSA) has been theoretically proven an optimal approach for accurate and quick detection of changes in the characteristics of a running (random) process. In addition, SSA is a model-free method and no parametric models have to be assumed for different types of anomalies, which makes it a universal anomaly detection scheme. In this paper, we introduce an adaptive parameter and component selection mechanism based on coherence for basic SSA method, upon which we built up a sliding window online anomaly detector in CRNs. Our experimental results indicate great accuracy of the SSA-based anomaly detector for multiple anomalies.

An Adaptive Primary User Emulation Attack Detection Mechanism for Cognitive Radio Networks

“…PUE attacks can be easily implemented in CRNs. It introduces great overhead on cognitive radio communication and causes chaos in dynamic spectrum sensing [9,10]. However, defense against the PUE attacks is nontrivial because traditional authentication and authorization (AA) methods are no longer applicable to CR systems.…”

A Survey on Simulation Tools and Testbeds for Cognitive Radio Networks Study

“…In addition, due to the complexity and open feature of the system, CRNs are vulnerable to several types of malicious attacks, including not only the traditional network threat such as jamming, eavesdropping, and MAC layer attack, but also certain CRN specific attacks such as control channel jamming, sybil attack, black hole attack and some newly developed types of attacks [29], for example, the Primary User Emulation (PUE) Attacks [23], [24], [25].…”