What are the key challenges that ML-powered physical layer security faces in 6G networks, and what future research directions are suggested to address them?

Key challenges include adversarial attacks on ML models, high processing demands, data privacy risks, and broader 6G security threats such as quantum computing, AI-driven attacks, and dynamic or heterogeneous network environments. Suggested future directions in the source are adversarial training, certified robustness, explainable AI, federated learning, quantum-resistant encryption, adaptive security frameworks, next-generation PLS protocols, and edge computing.

One central challenge is that the ML models used for PLS are themselves attack surfaces. An adversary could poison training data, perturb inputs during real-time inference to force erroneous security decisions, or exploit the general lack of robustness of many ML models, which is especially dangerous in high-stakes areas like autonomous vehicles and healthcare. The chapter also names high processing needs and data privacy risks as challenges, while describing broader 6G issues that complicate ML-powered PLS, such as quantum computers breaking RSA or ECC, AI-driven attacks bypassing traditional defenses, and heterogeneous networks with edge nodes and network slices. To address these, the source suggests adversarial training on clean and deliberately corrupted data, certified robustness that mathematically proves resistance to adversarial inputs, explainable AI to reveal weaknesses, federated learning that keeps training data localized, quantum-resistant lattice- and code-based encryption, adaptive ML-driven security frameworks that adjust security parameters in real time, next-generation PLS protocols with improved authentication and secure beamforming, and edge computing to reduce processing burdens.