Optimal Detection Technique for Primary User Emulation Attack in Cognitive Radio Networks

Abstract

The primary user emulation attack (PUEA) is one of the most common attacks affecting the physical layer of the cognitive radio network (CRN). In this attack, a malicious user or a selfish user mimics the signal characteristics of the primary user (PU) to deceive the legitimate secondary user (SU) causing it to leave the available channel while the real PU is absent hence, detecting this attacker is vital in building a real CRN. In this paper, the PUEA is detected based on the Time difference of Arrival (TDOA) localization technique using the particle swarm optimization (PSO), novel bat algorithm (NBA), and the modified particle swarm optimization (MPSO) to minimize the localization error from the TDOA measurement and comparison is made among the three algorithms in term of the localization accuracy, convergence rate, computation time via simulation using the MATLAB simulation tool by running the monte Carlo 1000 times. The performance of the techniques was evaluated using the mean square error (MSE) and cumulative distribution function (CDF) and the MPSO algorithm out-performed the PSO and the NBA