用智能探测防御毫米波偷波攻击，兼顾安全与通信质量
Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attacks
- 用强化学习动态控制感知通信一体化探测动作
- 检测率92.8%，用户平均信干噪比超13 dB
- 适合研究无线安全与智能感知融合的学者
毫米波通信系统面临日益严重的偷波攻击威胁。本文提出一种基于深度强化学习（DRL）的主动防御框架，利用集成感知与通信（ISAC）能力进行智能威胁评估。DRL代理基于近端策略优化（PPO）算法，动态控制ISAC探测行为以识别可疑活动。引入高强度课程学习策略，确保训练中成功检测，克服安全任务中的复杂探索难题。最终，代理学会鲁棒且自适应的策略，在保障通信性能的同时提升安全性。数值结果表明，该框架平均检测率达92.8%，用户平均信干噪比（SINR）保持在13 dB以上。
原文摘要 · Abstract (English)
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.
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