现代电子技术2026，Vol.49Issue(13)：83-89,7.DOI:10.16652/j.issn.1004-373X.2026.13.013
基于特征重构与MIR-BiLSTM的入侵检测方法
Intrusion detection method based on feature reconstruction and MIR-BiLSTM
摘要
Abstract
In view of the low detection accuracy caused by high computational complexity and poor adaptability to unbalanced data in current deep learning based intrusion detection models,this paper designs a hybrid detection model based on feature reconstruction and lightweight MIR-BiLSTM.Firstly,the SMOTE-ENN algorithm combining oversampling and undersampling is used to eliminate the category imbalance.Secondly,the improved stacked auto encoder(SAE)is designed to downscale and enhance the input features,and the low-dimensional robust feature representation is extracted by unsupervised pre-training.Next,the MIR-BiLSTM architecture including deformable convolutional LBL module is constructed,which achieves efficient feature extraction by combining multi-branch spatial modeling and bi-directional timing analysis.Finally,a staged training strategy is used to optimize the feature-task fitness.Experiments show that the proposed model requires a parameter count of only 2.76 MB(51.3%lower than MobileViT)and achieves an accuracy of 98.15%and an F1-score of 97.35%on the test set,which outperforms the existing lightweight intrusion detection models.To sum up,it provides an effective solution for edge computing environments.关键词
入侵检测/不平衡处理/SAE/MIR-BiLSTM/Leaky ReLU/注意力机制Key words
intrusion detection/imbalance processing/SAE/MIR-BiLSTM/Leaky ReLU/attention mechanism分类
信息技术与安全科学引用本文复制引用
崔颖,李会格,杨雪蓉,卢开喜..基于特征重构与MIR-BiLSTM的入侵检测方法[J].现代电子技术,2026,49(13):83-89,7.基金项目
国家自然科学基金资助项目(12401696) （12401696）
国家自然科学基金资助项目(61702234) （61702234）
船舶总体性能创新研究开放基金(25422217) （25422217）
