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The primary user emulation attack (PUEA) is one of the common threats incognitive radio networks (CRNs), in this problem, an attacker mimics thePrimary User (PU) signal to deceive other secondary users (SUs) to make themleave the white spaces (free spaces) in the spectrum assigned by the PU. Inthis paper, the PUEA is detected and localized using theTime-Difference-Of-Arrival (TDOA) localization technique. Particle SwarmOptimization (PSO) algorithms are proposed to solve the cost function of TDOAmeasurements. The PSO variants are developed by changing the parameters of thestandard PSO such as inertia weight and acceleration constants. Theseapproaches are presented and compared with the standard PSO in terms ofconvergence speed and processing time. This paper presents the first study ofdesigning a PSO algorithm suitable for the localization problem and will beconsidered as a good guidance for applying the optimization algorithms inwireless positioning techniques. Mean square error (MSE) and cumulativedistribution function (CDF) are used as the evaluation metrics to measure theaccuracy of the proposed algorithms. Simulation results show that the proposedPSO approaches provide higher accuracy and faster convergence than the standardPSO and the Taylor series estimation (TSE).

Particle Swarm Optimization Approaches for Primary User Emulation Attack Detection and Localization in Cognitive Radio Networks