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.

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入，汽车信息安全日益突出。车载网络系统负责控制智能互联车辆的状态，并严重影响行车安全。这项研究专注于一种基于时间序列预测的深度学习技术，即长短期记忆（LSTM）。提出了一种基于两种数据格式的异常检测算法，以检测篡改攻击下控制器局域网（CAN）总线的异常行为。提出了五种形式的损失函数，并用于比较测试结果以确定最终结果。