DCACA : Dual-Model Consensus-Based Anti-Risk Confidence Allocation Trust Management in IoVs

Cheong, Chaklam and Song, Yujie and Cao, Yue and Zhang, Yu’ang and Wang, Haoxiang and Ni, Qiang (2024) DCACA : Dual-Model Consensus-Based Anti-Risk Confidence Allocation Trust Management in IoVs. IEEE Internet of Things Journal. ISSN 2327-4662

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Abstract

With the development of Internet of Vehicles (IoVs), data security emerges as a significant challenge, especially regarding data tampering and the spread of false information. While cryptography technologies tackle external security threats, they fall short in addressing internal security threats, such as authorized malicious vehicles tampering with and spreading false information. Consequently, trust management becomes a crucial technology, focusing on the analysis and identification of internal inappropriate behaviors to ensure safe interactions among vehicles. This paper explores the effective integration of trust opinions provided by Roadside Units (RSUs) into trust evaluations in IoVs, ensuring the comprehensiveness and accuracy of trust evaluations. We propose a Dual-model Consensus-based Anti-risk Confidence Allocation trust management scheme (DCACA) in IoVs. Specifically, DCACA utilizes direct trust, indirect trust, and global trust, to evaluation the trustworthiness of vehicles. Furthermore, to address the potential untrustworthiness of network entities (RSUs and vehicles), DCACA employs a dual-model consensus mechanism operates two processes of reaching consensus, including Real-time Collection Consensus Mechanism (RCCM) and Matrix-based Consensus Mechanism (MCM). RCCM is based on real-time collected trust opinions, reaching consensus to identify potential malicious trust opinions. MCM utilizes trust opinion matrices to collect trust opinions and achieves consensus through the elements in these matrices, identifying the sources of malicious trust opinions. Additionally, DCACA utilizes an anti-risk confidence allocation mechanism assigns confidence levels based on risk assessments, to mitigate the impact of malicious entities. Extensive experiments demonstrate that our scheme significantly outperforms other baseline schemes, exhibiting high levels of precision, recall, and F-Measure.