Modeling and Detecting Falsified Vehicle Trajectories Under Data Spoofing Attacks
Primary Investigator:
Yiheng Feng
Jun Ying, Yiheng Feng, Qi Alfred Chen, Z. Morley Mao
Abstract
Connected Vehicle (CV) and Connected and Autonomous Vehicle (CAV) technologies can greatly improve traffic efficiency and safety. Data spoofing attack is one major threat to CVs and CAVs, since abnormal data (e.g., falsified trajectories) may influence vehicle navigation and deteriorate CAV/CV-based applications. In this work, we aim to design a generic anomaly detection model which can be used to identify abnormal trajectories from both known and unknown data spoofing attacks. First, the attack behaviors of two representative and sophisticated known attacks are modeled. Then, Using driving features derived from transportation and vehicle domain knowledge, an anomaly detection framework is proposed. The framework combines a feature extractor and an anomaly classifier trained with known attack trajectories and can be applied to identify falsified trajectories generated by various attacks. In the numerical experiment, a highway segment with a signalized intersection is built in the V2X Application Spoofing Platform (VASP). To evaluate the generality of the proposed anomaly detection algorithm, we further tested the proposed model with several unknown attacks provided in VASP. The results indicate that the proposed model achieves high accuracy in detecting falsified attack trajectories from both known and unknown attacks and outperforms several baselines. Furthermore, we show the importance of integrating domain knowledge in the feature selection process.