BEPCD:一种基于集体学习的入侵检测框架,用于车载CAN总线
Bocheng Xu1, Fei Cao1, Xilong Li1
1Department of Cyberspace Security, Information Engineering University, Zhengzhou, China.
PeerJ. Computer science
|September 24, 2025
概括
新的BEPCD框架增强了车辆的安全性,它改善了控制器区域网络 (CAN) 和车辆互联网 (IoV) 系统的入侵检测. 这种方法精确地识别了各种网络威胁,提高了车辆的整体安全.
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:
- 智能汽车和汽车互联网 (IoV) 正在迅速发展,对汽车安全的担忧越来越大.
- 控制器区域网络 (CAN) 总线入侵威胁到关键车辆系统,但当前的检测方法在特征提取和各种攻击类型方面扎.
- 电子控制单元 (ECU) 通过CAN总线管理车辆功能,使其成为网络攻击的目标.
研究的目的:
- 为车载CAN总线系统提出一个新的入侵检测框架,基本组合和先驱类决定 (BEPCD).
- 加强在 IoV 环境中检测传统和新兴的网络攻击模式.
- 解决现有方法在特征提取和可检测攻击的多样性方面的局限性.
主要方法:
- 开发了一个15维特征模型,以分层描述CAN总线消息.
- 实现了多模型组合学习,具有Pioneer类选择器和基于信任的投票,用于精确的攻击分类.
- 分析了四种机器学习算法的特征重要性,以了解数据贡献.
主要成果:
- 该BEPCD框架有效地检测使用公共数据集对车辆CAN总线系统的入侵.
- 与现有的入侵检测框架相比,F1总体得分提高了1%至5%.
- 在检测重复攻击方面表现出大约77.5%的显著性能提升.
结论:
- BEPCD框架提供了一个强大的解决方案,用于检测IoV系统中的各种网络威胁.
- BEPCD通过提高CAN总线上入侵检测的准确性和范围来提高车辆安全性.
- 拟议的方法代表了在保护联网汽车免受复杂的网络攻击方面取得的重大进展.
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