计算机与现代化Issue(3)：119-126,8.DOI:10.3969/j.issn.1006-2475.2025.03.018
基于convLSTM的卷积神经网络的网络入侵检测方法
Network Intrusion Detection Method Based on Convolutional Neural Networks with convLSTM
摘要
Abstract
In the field of network intrusion detection,machine learning methods that manually extract features in feature engi-neering are generally used,but the manual feature extraction method is prone to losing important feature information;In addi-tion,different types of attack traffic play different roles in detection,and existing algorithms generally suffer from important infor-mation loss and low accuracy in identifying attack types.A hybrid algorithm based on Convolutional Long-Short Term Memory(convLSTM)and Convolutional Neural Networks(CNN)is proposed for anomaly traffic detection in response to the aforemen-tioned issues,Which directly use the payload of network traffic as data samples without manual extraction of complex traffic fea-tures,fully explores the structural features of traffic,extracts temporal and spatial features,and generates accurate intrusion de-tection feature vectors.The experimental results show that on the CIC-ISDS2017 dataset,the accuracy of the hybrid algorithm convLSTM-CNN in network intrusion detection reaches 99.39%.Compared with the simple DNN,SVM,LSTM,GRU-CNN and other models,it has a higher accuracy and lower false alarm rate,indicating that the algorithm improves the efficiency of abnor-mal traffic detection.关键词
网络安全/入侵检测/卷积长短期网络/卷积神经网络/深度学习Key words
network security/intrusion detection/convolutional long-short term networks/convolutional neural network/deep learning分类
信息技术与安全科学引用本文复制引用
张跃,郭子昕,黄益彬,颜涛..基于convLSTM的卷积神经网络的网络入侵检测方法[J].计算机与现代化,2025,(3):119-126,8.基金项目
企业自选科技资助项目(5246DR230010) （5246DR230010）
