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

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  • Cheong Chak Lam
  • Yujie Song
  • Yue Cao
  • Yu’ang Zhang
  • Bo Cai
  • Qiang Ni
Article number10
<mark>Journal publication date</mark>15/05/2024
<mark>Journal</mark>IEEE Internet of Things Journal
Issue number10
Number of pages16
Pages (from-to)18619-18634
Publication StatusPublished
Early online date14/02/24
<mark>Original language</mark>English


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 article 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 MEFPB mechanism. Specifically, MEFPB utilizes the Dempster-Shafer theory to fuse multidimensional 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.