Butterfly Optimization Algorithm using Penalty-Reward Analysis for Secure Sensing

Cognitive radio (CR) is the best candidate for the growing demands of wireless connectivity in the internet of things (IoT). In the CR network, sensing performance is enhanced through cooperative spectrum sensing (CSS). However, security issues in CSS are to be tackled for reliable sensing. The malicious users (MUs) threaten the spectrum sensing that forward false sensing information to the fusion center (FC). To this end, this paper proposes an optimum CSS employing a penalty-reward-based butterfly optimization algorithm (PRBOA). The optimum coefficient vector enables FC to degrade the sensing notifications of the different categories of MUs in contrast with the normal secondary users (SUs). The coefficient vector and threshold are employed in the soft decision fusion to produce the global decision at the FC. A novel penalty-reward scheme is introduced in this work that assigns a penalty to the reports of MUs and rewards the normal SUs. The PRBOA scheme proposed in this work shows improved sensing results.