Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attacks

TL;DR

This paper presents a proactive deep reinforcement learning framework utilizing ISAC for secure mmWave beamforming, effectively detecting beam-stealing attacks with high accuracy while preserving communication quality.

Contribution

It introduces a novel DRL-based proactive defense mechanism leveraging ISAC for adaptive attack detection in mmWave systems, with a curriculum learning strategy ensuring robustness.

Findings

Achieves 92.8% attacker detection rate.

Maintains average user SINR over 13 dB.

Demonstrates robustness of the proposed framework.

Abstract

Millimeter-wave (mmWave) communication systems face increasing susceptibility to advanced beam-stealing attacks, posing a significant physical layer security threat. This paper introduces a novel framework employing an advanced Deep Reinforcement Learning (DRL) agent for proactive and adaptive defense against these sophisticated attacks. A key innovation is leveraging Integrated Sensing and Communications (ISAC) capabilities for active, intelligent threat assessment. The DRL agent, built on a Proximal Policy Optimization (PPO) algorithm, dynamically controls ISAC probing actions to investigate suspicious activities. We introduce an intensive curriculum learning strategy that guarantees the agent experiences successful detection during training to overcome the complex exploration challenges inherent to such a security-critical task. Consequently, the agent learns a robust and adaptive policy that intelligently balances security and communication performance. Numerical results demonstrate that our framework achieves a mean attacker detection rate of 92.8% while maintaining an average user SINR of over 13 dB.