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Conference

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

Naga Naveena Chennupati
Jul 2026 · 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT) · pp. 71-77 · 0 citations · 24 references

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.

DOI Semantic Scholar
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