A Particle Swarm Optimization Based algorithm for Primary User Emulation attack detection

Security in cognitive radio networks is considered as an important problem that is attracting a lot of interest from researchers. One of the main security threats is the Primary User Emulation (PUE) attack, which aims to gain illicit access to the licensed channels. A PUE can mimic the same signal as the real primary user (PU) which requires secondary users (SUs) to free immediately the channel. This attack can result in service degradation, deny of service (DoS), and a considerable impact on the cognitive network. To identify the attacker, nodes need to precisely locate the source of the PU and identify the authenticity of the PU. In the literature, most of localization of unknown signal sources are based on ranging schemes, which measure the distance between the blind node and the anchors. These anchors are static with known positions and are located near the signal source for accurate position detection. The challenge is to have limited number of anchor nodes that can detect the location of the signal. In this paper, we propose a technique based on particle swarm optimization algorithm and the received signal strength indicator (RSSI) for the PU/PUE position detection to increase the detection accuracy and decrease the probability of false alarms.