Harnessing Machine Learning for 6G-Integrated Network Security: Opportunities and Challenges

This research provides a comprehensive analysis of the role of machine learning in enhancing security for future 6G networks. It reviews existing ML-based security approaches, identifies key opportunities such as intelligent intrusion detection, adaptive threat prediction, automated response mechanisms, and privacy-preserving security solutions. Additionally, the study highlights major challenges, including adversarial attacks, data privacy issues, computational complexity, and standardization limitations. The paper proposes future research directions toward developing secure, intelligent, and autonomous 6G network architectures through advanced AI-driven security frameworks.