Open Access

ARTICLE

Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

Wenming Wang^1,2,3,*, Zhiquan Liu¹, Shumin Zhang¹, Guijiang Liu¹

1 School of Computer and Information, Anqing Normal University, Anqing, 246133, China
2 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China
3 Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, 650221, China

* Corresponding Author: Wenming Wang. Email:

Computers, Materials & Continua 2024, 81(2), 2517-2545. https://doi.org/10.32604/cmc.2024.055184

Received 19 June 2024; Accepted 13 September 2024; Issue published 18 November 2024

Abstract

Advancements in the vehicular network technology enable real-time interconnection, data sharing, and intelligent cooperative driving among vehicles. However, malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users. Trust mechanisms serve as an effective solution to this issue. In recent years, many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes, incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area. In this paper, we propose a distributed vehicular network scheme based on trust scores. Specifically, the designed architecture partitions multiple vehicle regions into clusters. Then, cloud supervision systems (CSSs) verify the accuracy of the information transmitted by vehicles. Additionally, the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation model. Our proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among vehicles. Both theoretical and experimental analysis show that our scheme outperforms the compared schemes.

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Keywords

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