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

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.

Automated summary

Electronization and intelligentization are becoming the fundamental characteristics of modern automobiles. Automotive information security is increasingly highlighted with the deepening of intelligent network integration. An anomaly detection algorithm based on long short-term memory (LSTM) is proposed to detect abnormal behavior of the controller area network (CAN) bus, showing lower false positive rate and higher detection rate.