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包含伪造和伪装攻击的CAN流量日志的CrySyS数据集
András Gazdag1, Rudolf Ferenc2, Levente Buttyán3
1CrySyS Lab, Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary. andras.gazdag@crysys.hu.
Scientific data
|December 15, 2023
概括
本研究介绍了控制器区域网络 (CAN) 安全性的新数据集,这对于训练机器学习模型至关重要. 该数据集有助于为车辆网络开发更好的异常检测系统.
科学领域:
- 汽车网络安全 汽车网络安全
- 网络安全 网络安全
- 机器学习应用 机器学习应用
背景情况:
- 控制器区域网络 (CAN) 在车辆中很普遍,但已知存在安全漏洞.
- 现有的CAN安全异常检测方法通常需要广泛的数据集用于机器学习模型训练.
研究的目的:
- 介绍一套专门为开发和评估基于机器学习的CAN网络异常检测系统而设计的全新,全面的数据集.
- 促进加强汽车网络安全的研究.
主要方法:
- 收集了26个良性CAN网络流量的记录,总共超过2.5小时.
- 模拟了对良性数据的两种常见学术攻击,创建了一个被攻击的数据集.
- 通过同时修改一个或两个信号来生成攻击变化,以增强现实性.
主要成果:
- 现在可以使用一个新的数据集,包括良性和被攻击的CAN流量.
- 数据集包括原始流量数据和用于攻击生成的源代码,从而实现定制.
- 该数据集支持开发机器学习模型,用于识别CAN网络攻击.
结论:
- 发布的数据集解决了在CAN安全研究中需要实质性,现实的数据的需求.
- 这一资源将加速汽车网络更强大的异常检测系统的开发.
- 数据集和攻击生成代码的可用性促进了CAN安全方面的进一步研究和创新.
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