@article{Razavi_Berangi_2024, title={Reducing Attack Effectiveness in Cognitive Radio Networks}, volume={6}, url={https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/reducing-attack-effectiveness-in-cognitive-radio-networks/}, abstractNote={Cognitive radio is a revolutionary technology that made significant progress in the effective use of the frequency spectrum. The technology itself can be dynamically adjusted so that the proper utilization of the available radio spectrum can be made. According to various studies conducted, it is observed that the bulk of each frequency band allocated to users leaving unused. The cognitive radio can use these parts of the spectrum that called spectrum holes. Inherent nature of this technology creates the chance for the attacker in these networks. This vulnerability that created due to the inherent nature of cognitive radio technology, Can severely impact on the safety and quality of service in these networks. In this paper, we focus on the primary user emulation attack. In this attack, an adversary transmits signals whose characteristics emulate those of incumbent signals. We proposed the method for reducing the effects of primary user emulation attacks in cognitive radio networks. This method suggests the technique that can merge with spectrum sensing method and can resistant these networks against the primary user emulation attacks. Finally, with run some simulation, we examined the performance of this proposed method in detection of primary user emulation attacks in these networks. }, number={4}, journal={Majlesi Journal of Electrical Engineering}, publisher={OICC Press}, author={Razavi, Parastoo and Berangi, Reza}, year={2024}, month={Feb.}, keywords={Cloud computing, DDoS attacks, Machine Learning, deep learning techniques, . ,} }