Proposed particle swarm optimization approaches for detection and localization of the primary user emulation attack in cognitive radio networks

Abstract

The primary user emulation attack (puea) is considered to be one of the common threats in cognitive radio networks (crns). in this problem, an attacker emulates the primary user (pu) signal to deceive other secondary users (sus) and forcing them to leave the white spaces (free spaces) in the spectrum assigned before to the PU. The PUEA is detected and localized using the time-difference-of-arrival (TDOA) localization technique based on stochastic optimization algorithms. Particle swarm optimization (PSO) algorithms are proposed to minimize the cost function provided by the TDOA measurements and to increase the accuracy of the detection. The PSO variants are evolved by changing the parameters of the standard PSO such as inertia weight and acceleration constants. These approaches are presented and compared with the standard PSO in terms of convergence speed and processing time. This paper presents the first study of designing a PSO algorithm suitable for the localization problem and it will be considered as a good guidance for applying the optimization algorithms in wireless positioning techniques. Mean square error (MSE) and cumulative distribution function (CDF) are used as the evaluation metrics to measure the accuracy of the proposed algorithms. Simulation results show that the proposed PSO approaches provide higher accuracy and faster convergence than the standard PSO, social spider optimization (SSO), cuckoo search (CS), firefly optimization (FA) and Taylor series estimation (TSE) methods.