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-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 behaviors. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset -- a GPS security dataset for AVs comprising real-world data collected using an AV testbed, and simulated data representing urban traffic environments. To the best of our knowledge, this dataset is the first of its kind and has been publicly released for the global research community to address such security challenges.

翻译：自动驾驶车辆（AVs）高度依赖全球定位系统（GPS）等传感器与通信网络来实现自主导航。先前研究表明，GPS等网络易受欺骗和干扰等网络攻击，从而引发导航错误与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GPS入侵检测系统（GPS-IDS），一种基于异常的入侵检测框架，用于检测针对自动驾驶车辆的GPS欺骗攻击。该框架采用一种新颖的基于物理的车辆行为模型，将GPS导航模型集成到传统的动态自行车模型中，以精确表征自动驾驶车辆行为。通过机器学习分析从该行为模型提取的时序特征，以检测正常与异常的导航行为。GPS-IDS框架的性能在AV-GPS-Dataset上进行了评估——这是一个面向自动驾驶车辆的GPS安全数据集，包含使用自动驾驶测试平台采集的真实世界数据，以及代表城市交通环境的模拟数据。据我们所知，该数据集属同类首创，并已公开发布以供全球研究界应对此类安全挑战。