An Anomaly Behavior Analysis Framework for Securing Autonomous Vehicle Perception

As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are encountering more security challenges as their capabilities continue to expand. In recent years, adversaries are actively targeting the perception sensors of autonomous vehicles with sophisticated attacks that are not easily detected by the vehicles' control systems. This paper proposes an Anomaly Behavior Analysis framework to detect perception system anomalies and sensor attacks against an autonomous vehicle. The framework relies on temporal features extracted from a physics-based autonomous vehicle behavior model to capture the normal behavior of vehicular perception in autonomous driving. By employing a combination of model-based techniques and machine learning algorithms, the proposed framework distinguishes between normal and abnormal vehicular perception behavior. As part of our experimental evaluation of the framework, a depth camera blinding attack experiment was performed on an autonomous vehicle testbed and an extensive dataset was generated. The effectiveness of the proposed framework has been validated using this real-world data and the dataset has been released for public access. To our knowledge, this dataset is the first of its kind and will serve as a valuable resource for the research community in evaluating their intrusion detection techniques effectively.