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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - DCACA
T2 - Dual-Model Consensus-Based Anti-Risk Confidence Allocation Trust Management in IoVs
AU - Cheong, Chaklam
AU - Song, Yujie
AU - Cao, Yue
AU - Zhang, Yu’ang
AU - Wang, Haoxiang
AU - Ni, Qiang
PY - 2025/1/15
Y1 - 2025/1/15
N2 - 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.
AB - 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.
U2 - 10.1109/jiot.2024.3464746
DO - 10.1109/jiot.2024.3464746
M3 - Journal article
VL - 12
SP - 1890
EP - 1906
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 2
ER -