辽宁工程技术大学学报（自然科学版）2024，Vol.43Issue(1)：93-100,8.DOI:10.11956/j.issn.1008-0562.2024.01.012
多尺度卷积与双注意力机制融合的入侵检测方法
Intrusion detection method based on multi-scale convolution and dual attention mechanism
摘要
Abstract
In order to improve the accuracy of internet intrusion detection methods,an intrusion detection method combining convolution neural network and attention mechanism is proposed.Using Borderline-SMOTE oversampling algorithm and MinMax normalization to preprocess data,effectively alleviate the problem of large differences in the amount of intrusion data,and improve the detection performance of unbalanced data;the convolution neural network inception structure is used for multi-scale feature extraction of data,and the attention mechanism is used for dimension update to improve the accuracy of feature expression when the model processes massive data.The experiment shows that the average accuracy of the intrusion detection method is 99.57%.Compared with SVM,CNN,RNN,and BLS-GMM,the accuracy increases by 4.48%,1.35%,1.62%and 0.04%respectively,and the recall increases by 4.48%,1.36%,1.62%and 0.14%respectively.关键词
入侵检测/卷积神经网络/注意力机制/过采样算法/非平衡数据Key words
intrusion detection/deep learning/attention mechanism/oversampling algorithm/unbalanced data分类
矿业与冶金引用本文复制引用
陈虹,李泓绪,金海波..多尺度卷积与双注意力机制融合的入侵检测方法[J].辽宁工程技术大学学报（自然科学版）,2024,43(1):93-100,8.基金项目
国家自然科学基金项目(62173171) （62173171）
