Fast Detection for Cyber Threats in Electric Vehicle Traction Motor Drives
Fast Detection for Cyber Threats in Electric Vehicle Traction Motor Drives
复制标题
DOI：
10.1109/tte.2021.3102452
复制
发表时间：
2021-08
影响因子：
7
通讯作者：
Bowen Yang;Jin Ye;Lulu Guo
中科院分区：
文献类型：
--
作者：
Bowen Yang;Jin Ye;Lulu Guo
由于车载通信网络的快速发展，电动汽车的网络物理安全受到越来越多的关注，但现有文献主要集中在车辆层面，并没有明确解决电动汽车动力系统牵引电机驱动的网络威胁检测问题。因此，在本文中，我们提出了一种快速，无模型的方法来检测电动汽车牵引电机驱动器中的网络威胁，只有四个易于获取，值得信赖的传感器信号。首先，选取可信赖的电机电流信号，削弱车辆随机行驶周期的影响。然后，选择一组对大范围异常最敏感的创新时域电流特征来减少所需的观测数量，从而大大减少了计算负担和检测时间。其次，开发了四种二元分类器来检测网络威胁，并采用多数投票机制来降低虚警率。最后，通过硬件在环实时仿真验证了所提方法的有效性。验证结果表明，与传统的电流特征分析（CSA）相比，该检测方法的检测速度更快。此外，所提出的检测方法准确率高于98%，虚警率小于0.01%。
While cyber-physical security of electric vehicles (EVs) is gaining increased concerns due to the fast development of vehicle onboard communication networks, the existing literature focuses on the vehicle level and it does not explicitly address cyber-threat detection for the EV powertrain traction motor drives. Therefore, in this article, we propose a fast, model-free approach to detect cyber threats in EV traction motor drives with only four easy-to-get, trustworthy sensor signals. First, the trustworthy motor current signals are selected to undermine the impacts of the vehicle’s random driving cycles. Then, a set of innovative time-domain current features that are the most sensitive to a wide range of anomalies are selected to reduce the number of observations needed, thus vastly reducing the computational burden and the time-to-detect. Next, four binary classifiers are developed to detect cyber threats, while a majority vote mechanism is adopted to reduce the false alarm rate. Finally, the proposed method is validated by the real-time hardware-in-the-loop simulations. Validation results show that the proposed detection method achieves much faster detection compared with traditional current signature analysis (CSA). Furthermore, the proposed detection methods achieve an accuracy higher than 98% with the false alarm rate less than 0.01%.