Behavioral Analysis of Cognitive Radio Network Users for Malicious Activity Detection

Cognitive Radio Networks (CRNs) have emerged as a solution to spectrum scarcity by enabling dynamic spectrum access. However, their decentralized nature makes them vulnerable to malicious activities such as spectrum misuse and denial-of-service attacks. This paper presents a behavioral analysis framework for detecting malicious users in CRNs. The proposed methodology employs a hybrid approach, combining statistical feature extraction with machine learning classifiers to identify anomalous patterns indicative of malicious behavior. Key features such as transmission duration, channel hopping frequency, and spectrum usage patterns are analyzed to distinguish between legitimate and malicious users. The framework is evaluated using a simulated CRN environment, achieving detection accuracies exceeding 95% while maintaining low false-positive rates. The results highlight the efficacy of the proposed approach in enhancing CRN security. This study provides critical insights into behavioral modeling for secure spectrum management in dynamic wireless environments.