Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning
This addresses security challenges for autonomous vehicles, but the approach is incremental as it builds on existing RL methods for attack design.
The paper tackles the vulnerability of autonomous vehicles to attacks by proposing a reinforcement learning-based method for designing optimal stealthy integrity attacks on AV actuators, and demonstrates its effectiveness through simulation experiments.
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.