In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user\n(PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance\nto the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the\noccurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision\nbased on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along\nwith z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The\nproposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite\nmalicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious\nuser (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the\nexisting hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.
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