Securing Short Range Applications in 5G Cognitive Radio Using an AI-Based Analysis of Security Threats

With 5G technologies, cognitive radio (CR) has become a possible answer to maximise spectrum utilization and efficiency. But in the case of short-range applications particularly, CR also brings significant security concerns in addition to benefits. Emphasizing short-range uses, this paper uses 5G's framework to assess the security issues with cognitive radio systems. Among the discovered security hazards are main user emulation attacks, jamming attacks, spectrum sensing, Byzantine attacks, etc., driven by reputation. Chaotic Deep Belief Networks (DBN) for detection and mitigating purpose is proposed to overcome these challenges using artificial intelligence (AI) approaches. Emphasizing the need of strong security measures to ensure the integrity and dependability of communication networks, the analysis considers the unique characteristics of 5G-CR spectrum and short-range applications. The results shows that the proposed Chaotic DBN ranging from 92.5% to 94.2%, the DBN Classification approach had an accuracy of 95.5% on the training set.