湖北汽车工业学院学报2026，Vol.40Issue(1)：23-29,7.DOI:10.3969/j.issn.1008-5483.2026.01.005
基于LSTM-Attention的汽车CAN/CAN-FD协议入侵检测模型
An Intrusion Detection Model for Automotive CAN/CAN-FD Protocols Based on LSTM-Attention
摘要
Abstract
In response to potential security risks in CAN/CAN-FD protocols,an LSTM-Attention intru-sion detection model was proposed.Input sequences were first processed by an LSTM layer to extract hidden states;these states were then weighted and summed through an Attention mechanism,after which a multi-layer perceptron performed the final classification.DoS,fuzzing,and spoofing attacks were evaluated on the CAN-FD Intrusion dataset,and comparative experiments with several models were conducted on the Car-Hacking dataset.The results show that the proposed model achieves preci-sion,recall,and F1-score above 99.9%for all three attack categories;it captures attack patterns more effectively,delivers superior detection performance,and exhibits strong generalization capability.关键词
CAN/CAN-FD/入侵检测/LSTM/LSTM-AttentionKey words
CAN/CAN-FD/intrusion detection/LSTM/LSTM-Attention分类
交通工程引用本文复制引用
吴奇,王思山,司华超,张贵海,郭阳东,曹举阳..基于LSTM-Attention的汽车CAN/CAN-FD协议入侵检测模型[J].湖北汽车工业学院学报,2026,40(1):23-29,7.基金项目
湖北省重点研发计划项目(2023BAB169) （2023BAB169）
