Enhancing Cooperative Spectrum Sensing in Cognitive Radio Systems: Mitigating Byzantine Attacks with a Weighted Algorithm

Abstract

Cooperative spectrum sensing (CSS) is a key approach in cognitive radio (CR) systems for dealing with fading, shadowing, and concealed node problems. CSS improves detection performance by utilizing the spatial range that results from the cooperative secondary users (CSUs). As part of centralized CSS, these CSUs collaborate to share information with a fusion center (FC), which makes global decisions. However, malicious users (MUs) can significantly decrease the sensing operation's accuracy. The crucial problem of Byzantine attacks is addressed in this paper through a weighted algorithm for MU detection in CSS environments. The proposed weighted algorithm efficiently detects and eliminates the effects of MU. A comprehensive analysis utilizes simulations of how well the proposed algorithm performs. The results are provided in a series of plots that show how superior the proposed algorithm is in terms of its resistance to Byzantine attacks and its capacity to increase CSS's overall dependability in the cognitive radio network (CRN).