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Anomaly Detection for Controller Area Networks Using Long Short-Term Memory
IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2020-12-08 , DOI: 10.1109/ojits.2020.3043066
Vinayak Tanksale

The rapid expansion of intra-vehicle networks has increased the number of threats to such networks. Most modern vehicles implement various physical and data-link layer technologies. Vehicles are becoming increasingly autonomous and connected. Controller Area Network (CAN) is one such 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. Classical cryptographic approaches are resource intensive. There is a need for efficient security countermeasures for protecting the CAN from various attacks. In this article, we present a novel Long Short-Term Memory (LSTM) network to detect anomalies. Once trained, our system is capable of detecting anomalies in real-time and uses minimal resources. We report the results of our novel prediction algorithm that we use to select optimal LSTM network parameters. Our prediction algorithm 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:

使用长短期记忆的控制器区域网络异常检测

车载网络的迅速扩展增加了对该网络的威胁数量。大多数现代车辆都采用了各种物理和数据链路层技术。车辆正变得越来越自治,越来越互联。控制器局域网(CAN)是一种这样的串行总线系统,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。经典的加密方法需要大量资源。需要有效的安全对策来保护CAN免受各种攻击。在本文中,我们提出了一种新颖的长短期记忆(LSTM)网络来检测异常。经过培训后,我们的系统能够实时检测异常并使用最少的资源。我们报告了我们用于选择最佳LSTM网络参数的新颖预测算法的结果。我们的预测算法和异常检测引擎已经对来自真实汽车的数据进行了测试。我们介绍了实验结果并分析了我们的发现。
更新日期:2020-12-22
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