IM-ZDD:一个功能增强的反向映射框架,用于在车辆互联网中检测零日攻击
Tao Chen1, Gongyu Zhang1, Bingfeng Xu1
1College of Information Science and Technology & College of Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
一个新的框架,IM-ZDD,尽管数据有限,但有效地检测到车辆互联网 (IoV) 中的零日攻击. 它使用先进的AI来建模正常行为,实现高准确性和低误报警安全连接的车辆.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 车辆互联网 (IoV) 中的零日攻击是由于未知的模式和稀缺数据而造成的重大威胁.
- 传统的异常检测方法与复杂的IoV行为作斗争,导致精度低,错误报警高.
- 现有的方法无法充分解决 IoV 安全中的数据稀缺性挑战.
研究的目的:
- 为数据稀缺的IoV环境提出一个新的零日攻击检测框架,IM-ZDD.
- 用先进的生成对抗网络 (GAN) 和反向映射来增强正常行为建模.
- 为了在 IoV 安全中实现高检测准确度和低错误报警率.
主要方法:
- 一个两阶段的框架:使用条件GAN (CGAN) 来生成合成正常数据的功能增强,以及在这些数据上训练的反向映射模块.
- 使用多生成器,多歧视器CGAN,具有动态聚焦损失,用于高质量的合成数据生成.
- 雇佣了反向映射模块的对抗性训练,以学习正常IoV行为的紧表示.
主要成果:
- 在F2MD平台上,IM-ZDD实现了卓越的性能,平均AUC为98.25%,F1-Score为96.41%.
- 性能高于最先进的方法,高达4.4% (AUC) 和10.8% (F1-Score).
- 显示了3毫秒的中位检测延迟,满足实时检测要求.
结论:
- IM-ZDD框架为在数据稀缺的IoV环境中检测零日攻击提供了强大的解决方案.
- 拟议的功能增强和反向映射方法有效地解决了传统方法的局限性.
- IM-ZDD为车辆互联网提供了高精度,低延迟和可靠的安全解决方案.
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