Yang, Zhe, Zhang, Kuan, Lei, Lei, and Zheng, Kan (2019) A novel classifier exploiting mobility behaviors for Sybil detection in connected vehicle systems. IEEE Internet of Things Journal, 6 (2). pp. 2626-2636.

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Abstract

A Sybil attacker is able to obtain more than one identities and disguise as multiple vehicles in order to interfere the normal operations of the Connected Vehicle System (CVS). In this paper, we propose a novel classifier to detect Sybil attackers according to their mobility behaviors. Specifically, three levels of Sybil attackers are first defined according to their attack abilities. Through analyzing the mobility behaviors of vehicles, a learning-based model is used in the Central Server (CS) to extract mobility features and distinguish Sybil attackers from benign vehicles. Three classification algorithms are tested and compared, i.e., the Naive Bayes, Decision Tree, and Support Vector Machine. Furthermore, location certificates issued by Base Stations are used to resist location forgery by attackers. Based on the location certificates, the CS is able to evaluate the credibilities of uploaded locations using the Subjective Logic theory. In addition, we develop an edge betweenness-based community detection algorithm to handle the collusion among multiple Sybil attackers. Simulations are conducted based on a real-world vehicle trajectory dataset, which indicate that the proposed scheme is effective to resist Sybil attackers in CVS.

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