MVDetector: Malicious Vehicles Detection Under Sybil Attacks in VANETs

Yiming Wei, Weiye Qi, Zechuan Li, Yufan Han, Yingxu Lai

Abstract: A Sybil attack involves a Malicious vehicle node stealing fake identities to continuously generate fake vehicles on the road, creating an illusion of congestion and interfering with the normal traffic flow of legitimate vehicles. In the current traffic environment, vehicles cannot perform real-time authentication, allowing highly stealthy Malicious vehicle nodes to continue attacking and significantly impact traffic. Given the rapidly changing network topology in vehicular networks, high precision and speed are required for attack detection methods. This paper proposes a three-class classification method for Sybil vehicles, Malicious vehicles, and Normal vehicles based on BSM packets. This method utilizes multiple features and employs a sliding window with the Random Forest algorithm for classification. Compared with deep learning methods, this method has the advantages of strong interpretability and fast detection speed. Experiments demonstrate that this method achieves fast detection speeds and high accuracy. With a window size of 2, the method achieves precision and recall both greater than 94%.