对联网和自动驾驶汽车车载网络入侵检测系统的敌对攻击
Fatimah Aloraini1,2, Amir Javed1, Omer Rana1
1School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
连接车辆中的机器学习入侵检测系统容易受到对抗性攻击. 即使是简单的攻击也可以显著降低性能,并创建虚假报警,影响车辆安全.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 汽车工程 汽车工程
背景情况:
- 连接和自动驾驶汽车 (CAV) 依赖机器学习 (ML) 来实现高级功能.
- 在车载网络 (IVN) 中基于ML的入侵检测系统 (IDS) 对安全至关重要.
- 敌对攻击对这些系统的可靠性构成重大威胁.
研究的目的:
- 调查IVN中基于ML的IDS对对抗性攻击的脆弱性.
- 评估IDS对操纵的易感性,特别是考虑到IVN数据的简单性与感知模型相比.
- 提出和评估一种针对IVN IDS的新型对抗性攻击方法.
主要方法:
- 开发了一种使用在机载诊断端口数据上训练的替代IDS进行黑盒对抗性攻击.
- 在现实的IVN流量限制下模拟攻击.
- 对基线IDS和最先进的模型 (MTH-IDS) 评估了攻击的有效性.
主要成果:
- 证明了两种IDS模型对拟议攻击的重大脆弱性.
- 在F1得分上取得了显著的降低,从基线的95%降至38%,MTH-IDS的97%降至79%.
- 发现引发虚假警报是一个特别有效的对抗策略,侵蚀信任.
结论:
- 基于IVN的IDS,尽管简单,但对对抗性操纵具有关键的脆弱性.
- 这些漏洞对车辆安全和用户信任构成威胁.
- 在开发IVN IDS和响应其警报时需要仔细考虑.
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