Abstract—Cognitive radios are characterized as intelligent radios that can detect, learn, and adapt; they perceive their operational environment and gather information via experience. Future wireless networks are anticipated to establish a dispersed, intelligent platform for wireless communication, sensing, and computation, necessitating the complex integration of the physical and digital realms in a smooth and sustainable fashion. This research is based on the analysis and the optimal solution of security attacks related to the cognitive radio network. The risks associated with both infrastructure-less and infrastructure-based cognitive radio networks are also covered. We address the frequently disregarded longer-term behavioral changes that are imposed by such attacks through the learning capabilities of cognitive radio network in addition to the short-term consequences of attacks on cognitive radio network performance. However, a few of them considered the punishment of attackers and ignored the effective measures to punish them. In this paper, a new sanction mechanism based on cognitive trust value is proposed. To deal with this issue, a hierarchical architecture cluster heads and data fusion center, the trust value of cognitively engaged users was managed. Fusion Center would punish bad users because they would decline their confidence; it is, therefore, essential to guarantee the safety of the network through a distinction between attack users. A simulation setup based on MATLAB is built for each step of the proposed system. The simulation results show the effectiveness of the proposed architecture in detecting attacks with a detection rate of over 80%.
 

Keywords—security, Cognitive Radio Network (CRN), attack detection, punishment, optimize, cluster heads

Cite: Himanshu Sharma, K. Kishore Kumar, G. Nithya, V. Ravi Kumar, Shashi Kant Dargar, and Amit Gupta, “Punishment Mechanism for Cognitive Radio Networks in Advanced Wireless Communication Systems," Journal of Communications, vol. 20, no. 5, pp. 619-631, 2025.


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