According to the chapter, what role does machine learning play in enhancing physical layer security for 6G wireless communication?

Machine learning improves physical layer security for 6G by enabling real-time threat and anomaly detection, adaptive key management, jamming detection and mitigation, predictive channel estimation, and signal fingerprint verification. This makes security smarter, faster, and more adaptable than traditional fixed cryptographic methods.

According to the chapter, ML-powered physical layer security (PLS) helps 6G networks handle threats that traditional upper-layer encryption and authentication may not fully address. ML models can examine large volumes of physical-layer data, such as channel conditions, signal quality, and interference patterns, to spot unusual behavior like jamming or eavesdropping. They can also distinguish legitimate user signals from attacker signals using physical-layer fingerprints, manage secure key distribution and rotation adaptively, and adjust error correction mechanisms to match current channel conditions. In addition, ML supports jamming detection and automatic responses such as switching frequency bands or changing transmission power, and it improves channel estimation by predicting future channel changes so that encryption, power levels, and modulation can be adjusted proactively. Overall, ML integration makes PLS adaptive and robust for the dynamic, large-scale 6G environment.