现代信息科技2026，Vol.10Issue(8)：173-177,183,6.DOI:10.19850/j.cnki.2096-4706.2026.08.031
基于深度学习的车载CAN总线入侵检测方法研究
Research on Intrusion Detection Method for Automotive CAN Bus Based on Deep Learning
徐进 1樊龙 1陆续1
作者信息
- 1. 中国航空工业集团公司西安航空计算技术研究所,陕西 西安 710068
- 折叠
摘要
Abstract
To address the severe security threats faced by the inherently insecure Controller Area Network(CAN)bus in the context of vehicle intelligence,this paper explores an efficient intrusion detection method.It constructs a complete process from data preprocessing and feature engineering to model training,and utilizes a combination of Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for accurate identification of abnormal communication patterns.Experimental results on a public dataset show that the proposed method achieves a detection accuracy of 97.8%for various common attacks,significantly outperforming traditional rule-based detection methods.This confirms the effectiveness of the deep learning model for in-vehicle CAN bus intrusion detection,providing a highly accurate and practical technical solution to automotive cybersecurity challenges.关键词
CAN总线/入侵检测/注意力机制/深度学习Key words
CAN bus/intrusion detection/Attention Mechanism/Deep Learning分类
信息技术与安全科学引用本文复制引用
徐进,樊龙,陆续..基于深度学习的车载CAN总线入侵检测方法研究[J].现代信息科技,2026,10(8):173-177,183,6.
