Next-Generation Cyber Defense in 6G Networks: An AI-Driven Optimization Framework

Abstract

The increased attack surface of the sixth generation (6G) network makes old security measures ineffective. In the current paper, the AI-based optimization framework is suggested, combining deep reinforcement learning (DRL) to respond to adaptive threat mitigation and federated learning (FL) to privacy-preserving anomaly detection on distributed 6G nodes. The caching algorithm of query fragment achieves a 34% computation overhead with no associated drop in detection faithfulness. When tested on a 6G Massive IoT dataset and O-RAN simulation, the framework has a 97.8% detection rate against DDoS attacks, data injection attacks, and adversarial evasion attacks. Compared to current AI-based intrusion detection systems, it reduces false positives by 42 percent and shortens response time by 58 percent. Findings affirm that next generation wireless infrastructures require adaptive, intelligent security.