Multidimensional Trust Evidence Fusion and Path-Backtracking Mechanism for Trust Management in VANETs

Lam, Cheong Chak and Song, Yujie and Cao, Yue and Zhang, Yu’ang and Cai, Bo and Ni, Qiang (2024) Multidimensional Trust Evidence Fusion and Path-Backtracking Mechanism for Trust Management in VANETs. IEEE Internet of Things Journal. ISSN 2327-4662

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Abstract

With the development of Vehicular Ad-hoc Networks (VANETs), several data security challenges are revealed, such as data hijacking and interception. Although vehicles are authorized, malicious behaviors still be carried out. Security lapses may lead to potential accidents, which emphasizes the importance of laying a solid security foundation for VANETs. Thanks to the base security layer provided by cryptography technologies, security problems can be solved in VANETs to avoid accidents. However, trust management focuses on the analysis and identification of misbehavior, to ensure secure interactions among vehicles, and preserve data integrity against security issues. This paper explores trust assessments that consider the transmission path of message as a novel indicator, to provide a comprehensive and accurate trust assessment. We propose a Multidimensional trust Evidence Fusion and Path-Backtracking mechanism for trust management scheme (MEFPB) in VANETs. MEFPB integrates the multidimensional trust evidence fusion and path-backtracking mechanism. Specifically, MEFPB utilizes the Dempster-Shafer theory to fuse multi-dimensional indicators (direct trust, indirect trust, and transmission path of message) for evaluating the trustworthiness of vehicles. The direct and indirect trust are supplied by the message-sending vehicle and its neighbors (i.e., other vehicles). The transmission path of message is provided by roadside units. Furthermore, the path-backtracking mechanism identifies and traces malicious behaviors based on the transmission path of message. Moreover, extensive experiments demonstrate that our scheme significantly outperforms other baseline schemes, exhibiting a high malicious behavior detection rate within VANETs.