A Reputation Evaluation Method for Vehicle Nodes in V2X

doi:10.3969/j.issn.1671-1122.2024.05.006

Abstract

Abstract:

The advancements in Vehicular Networks communication technologies facilitate the exchange and sharing of traffic information, thereby significantly enhancing travel efficiency. However, the openness of V2X networks increases the vulnerability of traffic entities to attacks from malicious vehicles, potentially leading to severe consequences. Addressing this issue, this paper proposed a reputation evaluation method for vehicle nodes in V2X. Initially, a partitioned blockchain network for vehicle nodes in Vehicular Networks was introduced. Subsequently, local reputation values were calculated based on trust among vehicle nodes and auxiliary trust from infrastructures, combined with the use of deep learning for dynamically computing global reputation values. This enabled the identification of optimal data sharing nodes based on global reputation scores. Finally, to enhance storage technology, partitioned blockchain technology was employed to ensure the integrity and traceability of reputation values and traffic information. Simulation results demonstrated that the proposed method outperformed comparative methods in accurately identifying malicious nodes, as evidenced by higher precision and recall rates.

Key words: V2X, reputation evaluation, blockchain, deep learning

CLC Number:

TP309

TIAN Zhao, NIU Yajie, SHE Wei, LIU Wei. A Reputation Evaluation Method for Vehicle Nodes in V2X[J]. Netinfo Security, 2024, 24(5): 719-731.

Figures/Tables 12

References 20

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输入	描述
$L{{T}_{ij}}$	由数据请求者${{v}_{i}}$计算的数据提供者${{v}_{j}}$的本地信誉值
$GT_{ij}^{t-\Delta t}$	数据提供者${{v}_{j}}$前一时段历史信誉值
$GT_{ij}^{t-2\Delta t}$	数据提供者${{v}_{j}}$前两个时段的历史信誉值
$GT_{ij}^{t-3\Delta t}$	数据提供者${{v}_{i}}$前三个时段的历史信誉值
$attac{{k}_{j}}$	数据提供者${{v}_{j}}$充当恶意节点累积攻击次数
${{x}_{i}}$	数据请求者${{v}_{i}}$当前横坐标
${{y}_{i}}$	数据请求者${{v}_{i}}$当前纵坐标
${{x}_{j}}$	数据提供者${{v}_{j}}$当前横坐标
${{y}_{j}}$	数据提供者${{v}_{j}}$当前纵坐标
${{x}_{\text{RSU}}}$	数据请求者${{v}_{i}}$所属RSU的横坐标
${{y}_{\text{RSU}}}$	数据请求者${{v}_{i}}$所属RSU的纵坐标

参数名称	参数数值
仿真时间/h	12
车辆节点数量/个	300
恶意节点占比	[0,50%]
车辆节点速度/m/s	[5,20]
车辆无线传输范围/m	[0,200]
信任阈值$G{{T}_{th1}}$、$G{{T}_{th2}}$	0.5、0.8
权重${{\alpha }_{1}}$、${{\alpha }_{2}}$、${{\alpha }_{3}}$	0.5、0.4、0.1
权重${{\beta }_{1}}$、${{\beta }_{2}}$	0.3、0.7
权重${{\psi }_{1}}$、${{\psi }_{2}}$、${{\psi }_{3}}$	0.4、 0.3、0.3