Infrastructure-enabled GPS Spoofing Detection and Correction

Feilong Wang, Yuan Hong, Xuegang Ban

Abstract: Accurate and robust localization is crucial for supporting high-level driving automation and safety. Modern localization solutions rely on various sensors, among which GPS has been and will continue to be essential. However, GPS can be vulnerable to malicious attacks and GPS spoofing has been identified as a high threat. GPS spoofing injects false information into true GPS measurements, aiming to deviate a vehicle from its true trajectory, endangering the safety of road users. With various types of vehicle-based sensors emerging, recent studies propose to detect GPS spoofing by deploying and identifying inconsistencies among multiple sensors. Yet, these methods often require sophisticated algorithms and cannot handle stealthy or coordinated attacks targeting multiple sensors. With roadside infrastructure becoming increasingly important in supporting emerging vehicle technologies and systems, this study explores the potential of applying infrastructure data in defending against GPS spoofing. We propose an infrastructure-enabled method by deploying roadside infrastructure as an independent, secured data source. A real-time detector, based on the Isolation Forest, is constructed to detect GPS spoofing. Once spoofing is detected, GPS measurements are isolated, and the potentially compromised location estimator is corrected using the infrastructure data. We test the proposed method using both simulation and real-world data and show its effectiveness in defending against various GPS spoofing attacks, including stealthy attacks that are proposed to fail the production-grade autonomous driving systems.