Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intensify with the widespread deployment of AVs, making it crucial to detect and mitigate such attacks. This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly Behavior Analysis (ABA)-based intrusion detection framework to detect GPS spoofing attacks on AVs. The framework uses a novel physics-based vehicle behavior model where a GPS navigation model is integrated into the conventional dynamic bicycle model for accurate AV behavior representation. Temporal features derived from this behavior model are analyzed using machine learning to detect normal and abnormal navigation behavior. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset - a real-world dataset collected by the team using an AV testbed. The dataset has been publicly released for the global research community. To the best of our knowledge, this dataset is the first of its kind and will serve as a useful resource to address such security challenges.

翻译：自动驾驶车辆（Autonomous Vehicles, AVs）高度依赖全球定位系统（Global Positioning System, GPS）等传感器和通信网络实现自主导航。先前研究表明，GPS等网络易受欺骗和干扰等网络攻击，从而引发导航错误及系统故障等严重风险。随着自动驾驶车辆的广泛部署，此类威胁预计将加剧，因此检测并缓解此类攻击至关重要。本文提出全球定位系统入侵检测系统（GPS-IDS）——一种基于异常行为分析（Anomaly Behavior Analysis, ABA）的入侵检测框架，用于检测针对自动驾驶车辆的GPS欺骗攻击。该框架采用新颖的基于物理的车辆行为模型，将GPS导航模型集成至传统动力自行车模型，以实现对自动驾驶车辆行为的精确表征。通过机器学习分析该行为模型生成的时序特征，检测正常与异常导航行为。GPS-IDS框架的性能在AV-GPS-Dataset上进行了评估——该真实世界数据集由研究团队利用自动驾驶车辆测试平台采集，并已面向全球研究社区公开发布。据我们所知，该数据集是同类首创，将为解决此类安全挑战提供宝贵资源。