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

arXiv: https://arxiv.org/abs/2508.02856 · HTML: https://mlsanity.com/paper/2508.02856/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [A Survey of Beam Management for mmWave and THz Communications Towards 6G](https://mlsanity.com/paper/2308.02135/) (2023)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications](https://mlsanity.com/paper/2405.01816/) (2024)
- [SecBeam: Securing mmWave Beam Alignment against Beam-Stealing Attacks](https://mlsanity.com/paper/2307.00178/) (2023)
- [Robust Multi-Beam Secure mmWave Wireless Communication for Hybrid Wiretapping Systems](https://mlsanity.com/paper/2407.13972/) (2024)
- [Synergizing Covert Transmission and mmWave ISAC for Secure IoT Systems](https://mlsanity.com/paper/2502.11719/) (2025)
- [Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience](https://mlsanity.com/paper/2505.08032/) (2025)

## Abstract (arXiv)

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.