학술논문
Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration
Document Type
Periodical
Source
IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 24(9):9462-9475 Sep, 2023
Subject
Language
ISSN
1524-9050
1558-0016
1558-0016
Abstract
GPS spoofing attacks pose great challenges to connected vehicle (CVs) safety applications and localization of autonomous vehicles (AVs). In this paper, we propose to utilize transportation and vehicle engineering domain knowledge to detect GPS spoofing attacks towards CVs and AVs. A novel detection method using learning from demonstration is developed, which can be implemented in both vehicles and at the transportation infrastructure. A computational-efficient driving model, which can be learned from historical trajectories of the vehicles, is constructed to predict normal driving behaviors. Then a statistical method is developed to measure the dissimilarities between the observed trajectory and the predicted normal trajectory for anomaly detection. We validate the proposed method using two threat models (i.e., attacks targeting the multi-sensor fusion system of AVs and attacks targeting the intersection movement assist application of CVs) on two real-world datasets (i.e., KAIST and Michigan roundabout dataset). Results show that the proposed model is able to detect almost all of the attacks in time with low false positive and false negative rates.