用机器学习识别车联网中车辆总线的恶意攻击，提升行车安全。
Intrusion Detection in Internet of Vehicles Using Machine Learning
- 基于CiCIoV2024数据集，用机器学习区分不同类型的恶意CAN总线流量。
- 识别出针对油门、转速、车速和方向盘等关键参数的欺骗与拒绝服务攻击。
- 适合车联网安全研究者及智能汽车系统开发者参考。
车联网（IoV）通过增强连接性和智能系统推动了现代交通发展，但更高的连通性也带来了关键安全隐患，使车辆易受拒绝服务（DoS）和消息伪造等网络攻击。本项目旨在构建基于机器学习的入侵检测系统，利用CiCIoV2024基准数据集对恶意控制器局域网（CAN）总线流量进行分类。我们分析了多种攻击模式，包括针对油门位置（Spoofing-GAS）、转速（Spoofing-RPM）、车速（Spoofing-Speed）和方向盘角度（Spoofing-Steering_Wheel）等关键车辆参数的伪造攻击。初步结果表明，攻击类型与正常数据在结构上存在明显差异，构成了多类分类问题，为机器学习模型提供了坚实基础。
原文摘要 · Abstract (English)
The Internet of Vehicles (IoV) has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptible to cyber-attacks such Denial-ofService (DoS) and message spoofing. This project aims to develop a machine learning-based intrusion detection system to classify malicious Controller Area network (CAN) bus traffic using the CiCIoV2024 benchmark dataset. We analyzed various attack patterns including DoS and spoofing attacks targeting critical vehicle parameters such as Spoofing-GAS - gas pedal position, Spoofing-RPM, Spoofing-Speed, and Spoofing-Steering\_Wheel. Our initial findings confirm a multi-class classification problem with a clear structural difference between attack types and benign data, providing a strong foundation for machine learning models.
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