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

2025-08-04

AI Summary

Beam-spoofing attacks pose an escalating physical-layer security threat in millimeter-wave (mmWave) communications. Method: This paper proposes an integrated sensing and communication (ISAC)-enabled proactive defense framework that synergistically combines real-time ISAC sensing with a curriculum-learning-enhanced Proximal Policy Optimization (PPO) deep reinforcement learning algorithm to enable dynamic attack detection and adaptive beam security control. A multi-stage curriculum training strategy is designed to improve agent generalization and decision-making efficiency under complex, time-varying channel conditions. Contribution/Results: Experiments demonstrate an average attack detection rate of 92.8% and sustained user SINR above 13 dB, achieving significant physical-layer security gains without compromising communication quality. The framework offers a scalable, low-overhead proactive security paradigm for mmWave systems.

Technology Category

• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Defending mmWave systems against beam-stealing attacks
• Balancing security and communication performance adaptively
• Detecting attacks using ISAC and DRL-based proactive defense

Innovation

• Deep Reinforcement Learning for adaptive defense
• Integrated Sensing and Communications for threat assessment
• Curriculum learning ensures robust detection policy