GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles

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.