Physics-Based Attack Detection for Traction Motor Drives in Electric Vehicles Using Random Forest
Physics-Based Attack Detection for Traction Motor Drives in Electric Vehicles Using Random Forest
复制标题
使用随机森林对电动汽车牵引电机驱动进行基于物理的攻击检测
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Jin Ye
中科院分区：
文献类型：
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作者：
Bowen Yang;Lulu Guo;Jin Ye
随着电动汽车和车载通信网络的快速发展，现代电动汽车受到来自网络的潜在威胁。为了保障车辆的安全性和可靠性，迫切需要先进的攻击检测技术.在本文中，我们提出了一种基于物理的攻击检测方法，使用随机森林分类器。其核心思想是从可靠且易于获取的电机相电流信号中提取系统特征，并使用随机森林分类器搜索安全边界，以区分动力传动系统是否受到恶意网络攻击。通过MATLAB Simulink生成的仿真数据对所提出的方法进行了测试和验证。实验结果证明了利用电机相电流信号表征动力系统多个特征的可行性，并基于这些特征准确检测恶意攻击。
With the fast development of electric vehicles and vehicle onboard communication networks, modern electric vehicles suffer from potential threats from cyber networks. In order to secure vehicle safety and reliability, advanced attack detection techniques are in urgent need. In this paper, we propose a physics-based attack detection method using a random forest classifier. The key idea is to extract system features from the trustworthy and easy-to-get electric machine phase current signals, and use a random forest classifier to search a secure boundary to distinguish whether or not the powertrain system is under malicious cyber-attacks. The proposed method is tested and validated by simulation data generated from MATLAB Simulink. The results prove the feasibility of using electric machine phase current signals to represent multiple powertrain system features and accurately detect malicious attacks based on these extracted features.