Believe Yourself: A User-Centric Misbehavior Detection Scheme for Secure Collaborative Spectrum Sensing

Abstract

Collaborative spectrum sensing has been proposed recently to facilitate precise detection of Primary Users in Cognitive Radio networks. However, it simultaneously introduces new security issue that the selfish or even misbehaving users could cheat a secondary user by depriving its access opportunity. To address this problem, we propose a novel User-centric Misbehavior Detection Scheme (UMDS) in this paper to detect malicious behaviors in collaborative spectrum sensing. The basic idea of UMDS is motivated by the fact that a mobile user tends to trust the sensing report generated by itself rather than the reports from other nodes. Therefore, a secondary user could independently determine if a sensing partner is malicious or not by calculating the correlation between the secondary user's own reports and those of other sensing nodes. We also discuss how to further improve the performance of the UMDS by choosing an optimized threshold. The effectiveness and efficiency of the proposed scheme is demonstrated by extensive analysis and numerical results.