Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3340 2019 2019 Enhancing Malicious User Recognition Using Coot Optimization Algorithm with Bayesian Belief Network for Cognitive Radio Networks Department of Information Systems, Faculty of Computing and Information Technology at Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia Rania Rania As a dynamic paradigm, Cognitive radio networks (CRNs) in wireless transmission enable devices to intelligently adapt their communication parameter based on real-world spectrum availability. Spectrum sensing lies at the core of CRNs, where nodes continue to monitor the spectrum for underutilized or unused band detection. However, the presence of malicious users (MUs) has a significant impact reliability and performance of the network. MUs detection is indispensable to prevent interference or unauthorized access and ensure network integrity. Advanced techniques combining game theory, machine learning, and signal processing are used for effectively identifying and mitigating malicious activities. CRNs can ensure efficient spectrum utilization and enhance security in heterogeneous and dynamic environments by incorporating robust MU detection systems into spectrum sensing protocols. This article presents a Malicious User Recognition using the Coot Optimization Algorithm with Bayesian Belief Network (MUR-COABBN) technique for CRN. The MUR-COABBN technique exploits metaheuristics with a Bayesian machine-learning method for the classification of the MUs in the CRN. In the MUR-COABBN technique, the COA is initially used to choose better feature subsets. Moreover, the detection of MUs can be performed by the use of BBN. Finally, the parameter tuning of the BBN model is carried out using an improved seeker optimization algorithm (ISOA). The experimental evaluation of the MUR-COABBN technique takes place with respect to distinct aspects. The experimentation outcomes implied the improved performance of the MUR-COABBN methodology with other methods under distinct measures. Therefore, the MUR-COABBN model can effectually and accurately improve security in the CRN. 2025 2025 131 146 10.54216/JCIM.150211 https://www.americaspg.com/articleinfo/2/show/3340