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Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction

机译：基于时间序列预测的控制器区域网络（CAN）总线异常检测的应用

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Electronization and intelligentization are gradually becoming the basic characteristics of modern automobiles. With the continuous deepening of intelligent network integration, automotive information security has become increasingly prominent. The in-vehicle network system is responsible for controlling the state of intelligent connected vehicles and significantly affecting driving safety. This research focuses on one deep learning technique based on time series prediction, namely long short-term memory (LSTM). An anomaly detection algorithm based on two data formats is proposed to detect the abnormal behavior of the controller area network (CAN) bus under tampering attacks. Five forms of loss functions are proposed and used to compare the test results to determine the final one. The evaluation indicates that the anomaly detection algorithm based on LSTM algorithm has a lower false positive rate and a higher detection rate using the chosen loss function. (C) 2020 Elsevier Inc. All rights reserved.
机译：电子化和智能化逐渐成为现代汽车的基本特征。随着智能网络集成的不断深化，汽车信息安全变得越来越突出。车载网络系统负责控制智能连接车辆的状态，并显着影响驾驶安全性。本研究重点介绍了一种基于时间序列预测的一种深度学习技术，即长的短期记忆（LSTM）。提出了一种基于两种数据格式的异常检测算法，以检测控制器区域网络（CAN）总线在篡改攻击下的异常行为。提出了五种形式的损失函数，并用于比较测试结果确定最终的测试结果。评估表明，使用所选损耗功能的基于LSTM算法的异常检测算法具有较低的误率和更高的检测率。 （c）2020 Elsevier Inc.保留所有权利。

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