用强化学习设计隐蔽的自动驾驶执行器攻击，验证现有防御漏洞。
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning
- 基于强化学习生成隐蔽的车辆执行器干扰
- 模拟实验验证攻击可有效误导车辆行为
- 揭示当前安全控制器的防御局限性
随着自动驾驶车辆（AV）日益普及，其面临各类攻击的风险也随之增加，带来重大安全挑战。本文提出一种基于强化学习（RL）的方法，用于设计针对自动驾驶车辆执行器的最优隐蔽完整性攻击。同时，我们分析了现有先进强化学习安全控制器在应对此类攻击时的局限性。通过大量仿真实验，验证了所提方法的有效性与高效性。
原文摘要 · Abstract (English)
With the increasing prevalence of autonomous vehicles (AVs), their vulnerability to various types of attacks has grown, presenting significant security challenges. In this paper, we propose a reinforcement learning (RL)-based approach for designing optimal stealthy integrity attacks on AV actuators. We also analyze the limitations of state-of-the-art RL-based secure controllers developed to counter such attacks. Through extensive simulation experiments, we demonstrate the effectiveness and efficiency of our proposed method.
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