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一个全面的CAN IDS数据指南和Road数据集的介绍

Miki E Verma¹, Robert A Bridges², Michael D Iannacone²

¹Rewiring America, United States of America.

|January 22, 2024

概括

控制器区域网络 (CAN) 安全研究受到缺乏多样化,现实的数据集的阻碍. 本研究介绍了ROAD数据集,提供现实世界的CAN数据与隐形攻击,以改善入侵检测系统 (IDS) 的基准测试.

科学领域:

网络安全网络安全
汽车工程汽车工程
网络安全网络安全

背景情况:

控制器区域网络 (CAN) 在现代车辆中至关重要,但缺乏固有的安全性,使其易受攻击.
目前对CAN入侵检测系统 (IDS) 的研究受限于测试的高保真,现实的数据集的稀缺性.
现有的数据集通常具有模拟攻击或缺乏对物理效应的验证,阻碍了研究进展和可比性.

研究的目的:

为现有的开放CAN IDS数据集提供全面的指南和质量分析.
引入一个新的,高保真数据集,用于对CAN IDS研究进行基准测试.
为了解决当前数据集的局限性,并促进改善研究可比性和可重复性.

主要方法:

现有CAN IDS数据集的分类和质量分析,包括攻击类型,数据格式和真实性.
开发和发布真实ORNL汽车动力表 (ROAD) CAN IDS数据集,包含真实数据和各种隐形攻击.
提供信号翻译数据与原始CAN数据一起提供,以支持各种IDS测试方法.

主要成果:

现有的公开CAN IDS数据集有限,主要包括简单的伪造攻击或模拟数据缺乏保真性和物理效应验证.
新推出的ROAD数据集提供了超过3.5小时的真实车辆CAN数据,具有各种真实和模拟的攻击,包括高级和隐形变体.
该数据集包括原始CAN数据和信号时间序列,解决了IDS研究的关键差距.

结论:

适当的CAN IDS数据集的稀缺性严重阻碍了研究的可比性和可重复性.
ROAD数据集提供了一个急需的资源,用于对CAN入侵检测系统进行强有力的基准测试.
这项工作旨在通过更有效地开发和验证CAN安全解决方案来推进汽车网络安全领域.

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