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 work proposes an Anomaly Behavior Analysis approach to detect a perception sensor attack 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. To demonstrate the application of the framework in practice, we performed a depth camera attack experiment on an autonomous vehicle testbed and generated an extensive dataset. We validated the effectiveness of the proposed framework using this real-world data and released the dataset 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.

翻译：作为一种快速发展的信息物理平台，自动驾驶车辆在能力持续扩展的同时正面临日益严峻的安全挑战。近年来，攻击者开始针对自动驾驶车辆的感知传感器发起复杂攻击，这类攻击难以被车辆控制系统察觉。本文提出了一种基于行为异常分析的方法，用于检测针对自动驾驶车辆的感知传感器攻击。该框架从基于物理模型的自动驾驶行为模型中提取时序特征，以捕获自动驾驶过程中车辆感知的正常行为模式。通过融合基于模型的技术与机器学习算法，所提出的框架能够有效区分车辆感知行为的正常与异常状态。为验证该框架的实际应用效果，我们在自动驾驶车辆实验平台上实施了深度相机攻击实验，生成了大规模数据集，并利用真实世界数据验证了所提框架的有效性，同时公开了该数据集。据我们所知，该数据集是首个同类资源，将为研究社区评估入侵检测技术提供重要支撑。