Federated learning and deep learning-driven adaptive cryptography for context-aware elliptic curve Diffie-Hellman optimization and anomaly-aware key management in next-generation wireless networks

This study addresses critical security challenges in wireless networks, such as impersonation, spoofing, data leakage, and trust issues, by adopting a hybrid approach that integrates deep learning, federated learning, and adaptive security solutions. Key innovations include Federated Learning-Based Trust Evaluation (FL-TE), Reinforced Physical Layer Authentication (RPLA), Adaptive Artificial Noise (AAN) Strategy, Context-Aware Elliptic Curve Diffie-Hellman Optimization, and an Anomaly-Aware Hybrid Key Management System. These developments ensure decentralized, real-time, and robust security, while protecting users effectively. Simulation results demonstrate that the proposed model meets all essential performance criteria. Notably, Isostated Authentication achieves 96.8% accuracy, accelerates trust convergence to just 8 rounds (compared to 15 rounds in existing methods), improves secrecy by 22%, reduces key refresh time by 35%, and handles complex attacks using only 7% of the usual resources. These findings highlight the strong security capabilities of the framework in managing multiple wireless networks efficiently.