Detection of primary user emulation attacks based on compressive sensing in cognitive radio networks

Abstract

In this paper, we introduce the idea of compressive sensing (CS) into primary user emulation attack (PUEA) detection in cognitive radio networks. We can distinguish whether the signal transmitters are primary users or PUEAs by obtaining the locations of transmitters through processing the received signal strength (RSS) readings. Since the RSS has redundancy in spatial domain, we employ CS theory to save the number of measurement sensors and messages need to be collected. Additionally, the number of measurements is dominated by the number of PUEA being sensed, the conventional algorithms with fixed measurement number have poor performances in physical situations where number of PUEA is unknown and changing. So, we propose an adaptive orthogonal matching pursuit algorithm (AOMP) to adapt to the changing cases of PUEA. Simulation results show that the location reconstruction of changing PUEA by AOMP algorithm outperforms traditional OMP algorithm with better accuracy. As a result, the channel utilization can be greatly improved.