关键词: 控制器局域网总线, 车内网络, 入侵检测系统, 机器学习, 网络安全

Abstract: With the continuous expansion of intelligent car functions and the growth of user groups, the network security issues of intelligent cars have gradually arisen people’s attention. The numerous external interfaces of intelligent vehicles provide attackers with many opportunities to invade the invehicle networks (IVN). However, due to the absence of any mechanism to defend external attacks to the IVN, attackers can easily access the vehicle network and control the vehicle through external interfaces, leading to serious traffic accidents. At present, intrusion detection systems (IDS) targeting at IVN are considered as an effective method to defend network intrusions. This paper will propose a CNNLSTM method based on attention mechanism to detect CAN bus intrusions. The method first transforms CAN communication data into images, then uses convolutional neural network (CNN) to extract the features, and sends them into long short term memory(LSTM) network with attention mechanism to determine if the communication is anomalous. The experimental results show that the proposed method performs well under all metrics and can detect the CAN intrusions effectively.

Key words: controller area network (CAN), in-vehicle network(IVN), Intrusion detection system, machine learning, network security