A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

作     者：Pengcheng Wei Bo Wang Xiaojun Dai Li Li Fangcheng He Pengcheng Wei;Bo Wang;Xiaojun Dai;Li Li;Fangcheng He

作者机构：School of Mathematics and Information EngineeringChongqing University of EducationChongqingChina School of AutomationChongqing University of Posts and TelecommunicationsChongqingChina Department of Modern ServiceChongqing Energy Industry Technician CollegeChongqingChina College of Foreign Languages LiteratureChongqing University of EducationChongqingChina 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第1期

页      面：14-21页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by Chongqing Big Data Engineering Laboratory for Children Chongqing Electronics Engineering Technology Research Center for Interactive Learning Project of Science and Technology Research Program of Chongqing Education Commission of China. (No.KJZD-K201801601). 

主　　题：Controller area network bus packet In-vehicle network Attention mechanism Autoencoder 

摘      要：The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.