建模和分析不同网络攻击注入模式下连接的自动化车辆队伍的自我阻力
Dongyu Luo1, Jiangfeng Wang1, Yu Wang1
1Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuan Cun, Haidian District, Beijing 100044, China.
Accident; analysis and prevention
|February 8, 2024
概括
连接的自动化车辆 (CAV) 面临的网络攻击会破坏车队. 这项研究评估了不同的汽车追踪模型如何抵御这些网络威胁,为增强车辆和网络层安全提供了见解.
科学领域:
- 智能运输系统 智能运输系统
- 网络物理系统安全 网络物理系统安全
- 车辆动力学和控制控制
背景情况:
- 互联自动化车辆 (CAV) 整合了网络和物理层,以实现合作旅行,但网络攻击和资源限制威胁着车辆层的稳定性.
- 现有的研究往往假定汽车追踪模型适用于网络攻击,忽视了该模型对防御有效性的固有影响.
- 了解各种汽车跟踪模型对各种网络攻击的自我抵抗力,对于强大的车辆层建模至关重要.
研究的目的:
- 分析各种汽车跟踪模型对抗网络攻击的自我抵抗能力.
- 在网络威胁的背景下,为车载层建模提供理论基础.
- 评估网络攻击对中队稳定的影响,并制定防御策略.
主要方法:
- 从交通工程,物理统计和排队动态中审查了汽车跟踪模型.
- 将网络攻击分为明确的和隐含的类型.
- 开发了一种合作通用部队模型 (CGFM),并将其集成到脆弱模型 (VCIDM,VCOVM,VCPDM) 中,以模拟网络攻击的影响.
- 在驾驶员,车辆和环境维度上定义了六个指标,以评估排队的自我阻力.
主要成果:
- 该研究量化了网络攻击 (虚假信息,重播/延迟,通信中断) 对排队自我抵抗的影响.
- 易受攻击的模型表现出不同程度的耐受性,适应性和抵抗力.
- 拟议的指标有效地说明了在网络威胁下排队的行为.
结论:
- 这项研究为车辆层建模提供了理论基础,通过评估不同汽车追踪模型对网络攻击的自我抵抗力.
- 这些发现对于提高车载层内内源安全性和提高CAV网络层的入侵耐受性至关重要.
- 这项工作有助于实际开发更安全和更有弹性的连接自动化车辆系统.
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