Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning

• URL: http://arxiv.org/abs/2502.07839v1
• Date: Tue, 11 Feb 2025 03:01:05 GMT
• Title: Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning
• Authors: Pengyu Wang, Jialu Li, Ling Shi,
• Abstract summary: 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 to counter such attacks.
• Score: 11.836584342902492
• License:
• Abstract: 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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