An improved primary user emulation attack detection in cognitive radio networks based on firefly optimization algorithm

Cognitive Radio Network (CRN) has been developed to solve the spectrum scarcity problem. In this network, unlicensed users share the spectrum of primary users (PUs) using a spectrum sensing process while do not cause any interference to the PUs. The spectrum sensing process can be disrupted by a security problem called Primary User Emulation Attack (PUEA). In this paper, this problem is solved using localization defence model based on time-difference-of-arrival measurements using the firefly optimization. Cognitive Radio (CR) users cooperate together to detect and localize the attacker by comparing its location with the position of the PU. A firefly optimization algorithm is used to minimize the nonlinear least squares cost function and minimize the maximum likelihood cost function. Simulation results are compared with the previous Taylor series estimation method and show that the firefly optimization algorithm reduces the localization error and require less number of SUs needed for cooperation. It is also shown that the maximum likelihood method gives higher accuracy than the nonlinear least squares and Taylor series methods.