Adaptive Trust-Based Malicious user Detection in Spectrum Sensing for Cognitive Radio Networks using AI and Blockchain

Malicious user detection in spectrum sensing is a critical challenge in Cognitive Radio Networks (CRNs). Traditional rule-based mechanisms lack adaptability to dynamic behaviors, while existing AI techniques often overlook scalability and real-time constraints. This paper proposes a novel hybrid framework that integrates adaptive trust-based mechanisms with AI-powered anomaly detection and blockchain technology to achieve superior detection accuracy (>90%), energy efficiency (30% reduction), and scalability (supporting 500+ nodes with blockchain throughput >750 transactions/second). The framework dynamically updates trust scores using machine learning models and leverages blockchain for secure and transparent spectrum management. Comparative simulations demonstrate superior performance compared to existing methods. The proposed methodology addresses the limitations of static trust mechanisms and offers a robust solution for real-time malicious user detection in CRNs.