Vehicles are becoming increasingly autonomous and connected, leading to an increase in the types of security threats to vehicles. Controller Area Network (CAN) is a serial bus system that is used to connect sensors and controllers (Electronic Control Units – ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. There is a need for efficient security countermeasures for protecting the CAN from various attacks. In this paper, we present a novel process to efficiently design functions that can be used for anomaly detection. Our earlier work successfully demonstrated the use of Long Short-Term Memory (LSTM) Networks to perform anomaly detection. This paper focuses on the efficient design and testing of functions that are attack-resistant and can be used in our anomaly detection engine. Once trained, our system is capable of efficiently detecting anomalies in real-time. We report the results of our anomaly detection function design process. We also present the results of our overall anomaly detection engine that are used as inputs to our detection engine. Our function design process and anomaly detection engine have been tested on data from real automobiles. We present the results of our experiments and analyze our findings.

中文翻译：

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控制器局域网异常检测功能的设计

车辆变得越来越自主和互联，导致车辆的安全威胁类型增加。控制器局域网 (CAN) 是一种串行总线系统，用于连接车辆内的传感器和控制器（电子控制单元 - ECU）。ECU 在处理能力、存储、内存和连接性方面差异很大。需要有效的安全对策来保护 CAN 免受各种攻击。在本文中，我们提出了一种有效设计可用于异常检测的功能的新过程。我们早期的工作成功地展示了使用长短期记忆 (LSTM) 网络来执行异常检测。本文重点介绍抗攻击功能的高效设计和测试，可用于我们的异常检测引擎。一经训练，我们的系统能够有效地实时检测异常。我们报告了异常检测功能设计过程的结果。我们还展示了我们整体异常检测引擎的结果，这些结果用作我们检测引擎的输入。我们的功能设计过程和异常检测引擎已经在真实汽车的数据上进行了测试。我们展示了我们的实验结果并分析了我们的发现。