  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.
