微型电脑应用2025，Vol.41Issue(11)：26-29,34,5.
基于改进CNN的网络入侵检测技术
Network Intrusion Detection Technology Based on Improved CNN
摘要
Abstract
In order to improve the efficiency of network intrusion detection,an improved CNN model is proposed by integrating convolutional neural network(CNN),bidirectional gated recurrent units(BiGRU)and attention mechanism.The proposed model uses CNN to extract the local features of network traffic data,captures the time series features through BiGRU,and combines the attention mechanism to perform weighted processing on the different types of network traffic data.The proposed network intrusion detection model is applied to KDD Cup 99 dataset,and the detection accuracy of Normal,DOS,Probe,R2L and U2R attack types is 92.8％,93.6％,92.7％,93.3％and 91.4％,respectively.The proposed model is superior to the CNN model and the CNN-BiGRU model in four evaluation indexes under DOS attack types,and the ablation experiment verifies that BiGRU has more advantages in time series feature extraction than one-way GRU.This provides a reliable method for net-work intrusion detection in the field of network security,and has certain practical value.关键词
卷积神经网络/网络入侵检测/双向门控循环单元/注意力机制Key words
convolutional neural network/network intrusion detection/bidirectional gated recurrent units/attention mechanism分类
信息技术与安全科学引用本文复制引用
毛军强,马君亮..基于改进CNN的网络入侵检测技术[J].微型电脑应用,2025,41(11):26-29,34,5.基金项目
教育部产学合作协同育人项目(230701701075115) （230701701075115）
