BMSAD: behavior feature and multi-scenario-based Sybil attack detection method in vehicular networks

Jie Luo; Zhong Li

doi:10.1117/12.2667662

22 February 2023 BMSAD: behavior feature and multi-scenario-based Sybil attack detection method in vehicular networks

Jie Luo, Zhong Li

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258710 (2023) https://doi.org/10.1117/12.2667662
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

Vehicular networks improve traffic safety and efficiency by wireless communications among vehicles and infrastructures. However, security has always been a challenge to vehicular networks, which may cause severe harm to the intelligent transportation systems. Sybil attack is considered as a serious security threat to vehicular networks since the attacker can disseminate false messages with multiple forged identities. In this paper, we designed a behavior feature and multi-scenario-based Sybil attack detection method, named BMSAD in vehicular networks. In BMSAD, we propose solutions for two scenarios: normal traffic and traffic congestion. A node is allowed to verify the authenticity of another node by estimating their geographic distance based on received signal strength, and compare them to its claimed localizations. Then we design long- and short-term pattern to analyze the similarity of vehicles’ behavior features and trajectory for Sybil nodes detection, which act as the second line of defense in normal traffic scenario. At last, the Sybil nodes of the traffic congestion scenario are detected by logical inference based on kinematics theory. And experiment results demonstrate that the proposed scheme achieves high detection rate and low false positive rate.

Citation Download Citation

Jie Luo and Zhong Li "BMSAD: behavior feature and multi-scenario-based Sybil attack detection method in vehicular networks", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258710 (22 February 2023); https://doi.org/10.1117/12.2667662

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