RSS-Based Localization with Maximum Likelihood Estimation for PUE Attacker Detection in Cognitive Radio Networks

With the rapid proliferation of mobile users, the spectrum scarcity has become one of the issues that have to be addressed. Cognitive Radio technology addresses this problem by allowing an opportunistic use of the spectrum bands. In cognitive radio networks, unlicensed users can use licensed channels without causing harmful interference to licensed users. However, cognitive radio networks can be subject to different security threats which can cause severe performance degradation. One of the main attacks on these networks is the primary user emulation in which a malicious node emulates the characteristics of the primary user signals. In this paper, we propose a detection technique of this attack based on the RSS-based localization with the maximum likelihood estimation. The simulation results show that the proposed technique outperforms the RSS-based localization method in detecting the primary user emulation attacker.