面向车载自组织网络的混合信任管理方案研究

doi:10.3778/j.issn.1673-9418.2211032

摘要/Abstract

摘要： 随着智能交通的快速发展，车载自组织网络（VANETs）具有广阔的发展前景，但也面临多种安全威胁。针对车载自组织网络中可能存在的内部攻击者和虚假消息，提出了一种分布式的混合信任管理方案 HTMS-V。该方案考虑到车载自组织网络的特性，基于改进的主观逻辑模型结合直接信任和间接信任对网络中的车辆节点进行信任评估，基于节点间的交互记录建立节点间的信任关系；基于节点信任和节点间距离进行消息信任评估，并根据信任评估结果识别网络中的虚假消息和恶意节点。为了验证所提出方案的性能，设计了四种不同的攻击场景，在车辆网络仿真平台Veins上进行对比实验，测试HTMS-V方案在各种攻击场景下的表现。实验结果表明，HTMS-V方案能有效抵抗车载自组织网络中的各种攻击，在40%恶意节点率的情况下仍能识别大部分虚假消息和恶意节点，且HTMS-V方案的表现明显优于由主观逻辑模型和基于距离的加权投票构成的基线方案。

关键词: 车载自组织网络（VANETs）, 信任管理, 主观逻辑

Abstract: With the rapid development of intelligent transportation, vehicular ad hoc networks (VANETs) has broad development prospects, but also faces a variety of security threats. A distributed hybrid trust management scheme called HTMS-V is proposed for insider attackers and false message detection in VANETs. Specifically, considering the characteristics of VANETs, node trust is evaluated based on the improved subjective logic model. The evaluation combines direct trust and indirect trust, and the trust relationship between nodes is established based on interaction records. Message trust is evaluated based on node trust and the distance between nodes. And the scheme identifies false messages and malicious nodes based on the trust evaluation results. This paper tests the performance of the HTMS-V scheme under four attack scenarios. The simulation results show that the HTMS-V scheme effectively resists various attacks in VANETs and is able to identify most false messages and malicious nodes even if the malicious node ratio reaches 40%, and the performance of the HTMS-V scheme is obviously better than that of the baseline scheme which is composed of subjective logical model and distance based weighted voting.

Key words: vehicular ad hoc networks (VANETs), trust management, subjective logic

项丹, 陈泽茂. 面向车载自组织网络的混合信任管理方案研究[J]. 计算机科学与探索, 2024, 18(2): 516-525.

XIANG Dan, CHEN Zemao. Research on Hybrid Trust Management Scheme for VANETs[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(2): 516-525.

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