Adaptive Dynamic Adversarial Training with Real-Time Feedback in 6G

The emergence of 6G communications brings forth both unprecedented opportunities and security challenges. This work introduces a novel method, dynamic adversarial training incorporating real-time feedback (DAT-RTF), to bolster the resilience of machine learning models in 6G mmWave beamforming prediction against adversarial attacks. By enabling models to adapt to new threats, DAT-RTF maintains their robustness. This dynamic approach, leveraging continuous feedback from the network, adjusts and refines defense mechanisms to remain effective against a range of adversarial strategies. We tackle the issues of resource demand and overfitting, offering strategies to enhance performance without sacrificing security. Our method represents a significant advancement in protecting 6G networks from evolving adversarial threats.