在车辆到一切通信环境中使用Ensemble和基于聊学习的入侵检测系统框架
Muhammad Nadeem Ali1, Muhammad Imran1, Ihsan Ullah1
1Department of Software & Communications Engineering, Hongik University, Sejong-si 30016, Republic of Korea.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究引入了用于检测自动驾驶汽车中拒绝服务 (DoS) 攻击的新方法. 组合学习和八学习实现了高检测率,提高了智能运输系统的安全性.
科学领域:
- 智能运输系统中的网络安全
- 机器学习用于网络安全
背景情况:
- 自动驾驶汽车的运行依赖于车辆到一切 (V2X) 通信.
- V2X通信容易受到网络攻击,特别是拒绝服务 (DoS) 攻击.
- 无人驾驶汽车的DoS攻击会严重破坏自动驾驶汽车的功能和安全.
研究的目的:
- 开发和评估用于检测自动驾驶汽车中的DoS攻击的方法.
- 在基础设施 (V2X 模式 3) 和无基础设施 (V2X 模式 4) 场景中解决DoS检测.
- 将拟议的方法与现有的安全方案进行比较.
主要方法:
- 在V2X模式3 (基于基础设施) 中为DoS攻击检测提出了一个集体学习 (EL) 方法.
- 在V2X模式4 (无基础设施) 中引入基于八学习 (GL) 的方法来检测DoS攻击.
- 使用了UNSW-NB15数据集来评估EL和GL方法的性能.
主要成果:
- 整体学习方法实现了DoS攻击检测效率的98.82%.
- 八学习方法显示DoS攻击检测效率为99.16%.
- 两种建议的方法都超过了现有的DoS检测方案.
结论:
- 集体学习和八学习是检测自动驾驶汽车通信中的DoS攻击的有效策略.
- 提出的方法为智能运输系统提供了强大的安全解决方案.
- 这些发现有助于推进安全的自动驾驶技术.
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