在自适应巡航控制系统中,基于机器学习的网络攻击的检测和缓解
Yan-Tao Zhang1, Li Yang1, Hao Zhang1
1School of Medical Information, Wannan Medical College, Wuhu, 241000, P.R. China.
Scientific reports
|October 17, 2025
概括
适应式巡航控制 (ACC) 系统面临来自车辆到车辆 (V2V) 通信的网络安全风险. 一个新的机器学习模型,ACC异常检测和缓解 (ACCDM),有效地识别和缓解这些威胁,以确保驾驶安全.
科学领域:
- 网络安全 网络安全
- 汽车工程 汽车工程
- 机器学习 机器学习
背景情况:
- 车辆对车辆 (V2V) 通信增强了自适应巡航控制 (ACC) 功能,但引入了网络安全漏洞.
- 虚假信息注入 (FII) 攻击可以操纵V2V消息,增加碰撞风险并降低驾驶效率.
- 现有的ACC系统缺乏对复杂的V2V消息操纵的强有力的防御.
研究的目的:
- 调查三种不同的虚假信息注入 (FII) 攻击类型对车辆碰撞风险和驾驶效率的影响.
- 开发和评估基于机器学习的新型机载模型,ACC异常检测和缓解 (ACCDM),以提高ACC系统的弹性.
- 验证ACCDM在实时检测和缓解V2V网络攻击方面的有效性.
主要方法:
- 模拟了三种类型的虚假信息注入 (FII) 对ACC系统的攻击.
- 开发了一种机器学习模型 (ACCDM),用于实时监控车辆参数和异常检测.
- 在连续和集群攻击场景下进行模拟,以评估ACCDM的性能.
主要成果:
- 在检测源自V2V消息操纵的网络安全威胁方面,ACCDM表现出高准确度.
- 拟议的模型成功地在模拟攻击期间保持了安全的后续距离.
- ACCDM有效地减轻了网络攻击对ACC系统安全和驾驶效率的负面影响.
结论:
- 开发的ACCDM模型提供了一个强大的解决方案,用于加强ACC系统对V2V通信威胁的网络安全.
- 基于机器学习的异常检测是连接汽车实时威胁识别和缓解的可行方法.
- 通过加强ACC应对网络攻击的弹性,ACCDM有助于实现更安全,更有效的自动驾驶.
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