计算机技术与发展2024，Vol.34Issue(9)：88-93,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0201
基于改进CBAM和BiGRU的入侵检测模型
Intrusion Detection Model Based on Improved CBAM and BiGRU
摘要
Abstract
Existing methods suffer from the problems of unbalanced network traffic data,insufficient detection accuracy,and an increasing false alarm rate.We propose a network intrusion detection model based on Improved CBAM(Convolutional Block Attention Module),dilated convolution and BiGRU(Bidirectional Gated Recurrent Unit),which aims to solve the problems of the existing methods.Specifically,to cope with the problem of unbalanced data distribution,we employ the ADASYN(Adaptive Oversampling)algorithm for adaptive oversampling to balance the dataset.To address the problems of insufficient detection accuracy and increasing false alarm rates,in the feature extraction phase,we first introduce a three-layer dilated convolution to expand the range of the sensing field so as to com-prehensively capture the features of network traffic.Second,we employ an improved CBAM module to enhance the extraction capability of dilated convolution for advanced features.Finally,BiGRU is also introduced to capture the long-term dependencies between features more deeply to further enhance the performance of the model.Experimental results show that the proposed method has a higher accuracy of 99.51%and a lower false detection rate of 2.90%relative to other methods on the NSL-KDD dataset,which suggests that the proposed model is a feasible and effective approach to network intrusion detection tasks.关键词
网络入侵检测/膨胀卷积/卷积注意力模块/双向门控循环单元/ADASYN过采样Key words
network intrusion detection/dilated convolution/convolutional block attention module/BiGRU/ADASYN分类
信息技术与安全科学引用本文复制引用
许东园,曹争光,黄春麟..基于改进CBAM和BiGRU的入侵检测模型[J].计算机技术与发展,2024,34(9):88-93,6.基金项目
国家自然科学基金地区科学基金项目(62162052) （62162052）
