Reinforcement Learning-based Adversarial Attack Generation Examples in Connected and Autonomous Vehicles: A Case Study on Vehicular Platoons

Connected and Autonomous Vehicles (CAVs) are vulnerable to security risks due to their large dependence shared communication networks. Motivated by this limitation, existing literature has largely focused on the CAV security problems from cyber-attack detection and accommodation point of view. On the other hand, understanding adversarial models and adversarial examples are critical in verification and testing of attack detection and accommodation strategies. However, existing research lack such adversarial example studies in the context of CAVs. In this work, we address this gap and propose a framework for model-free reinforcement learning-based adversarial attack generation examples, which can be used for verification and testing attack diagnostic strategies. We show a simulation study to demonstrate the effectiveness of the proposed framework.