计算机与数字工程2024，Vol.52Issue(2)：468-472,5.DOI:10.3969/j.issn.1672-9722.2024.02.031
基于CNN-BiGRU-ResNet的网络入侵检测研究
Research on Network Intrusion Detection Based on CNN-BiGRU-ResNet
摘要
Abstract
Network intrusion detection is an important work in network security.It mainly judges the intrusion behavior through network,system and other information.It can detect the attack behavior in the network in time.The traditional network intru-sion detection method has the problems of low accuracy and high false alarm rate.Aiming at the above problems,a bi-directional gate control loop unit(BiGRU)is proposed.The method of network intrusion detection of convolutional neural network(CNN)and residual network(ResNet)is proposed.The method extracts the time series features by two-way gate control cycle unit and the local spatial features of convolutional neural network and residual network,and obtains the final classification results by using softmax classifier.The experiment shows that the method has better effect and higher accuracy than the GRU and RetNet based method.关键词
双向门控循环单元/卷积神经网络/残差网络/网络入侵检测Key words
bidirectional gating cycle unit/convolutional neural network/residual network/network intrusion detection分类
信息技术与安全科学引用本文复制引用
包锋,庄泽堃..基于CNN-BiGRU-ResNet的网络入侵检测研究[J].计算机与数字工程,2024,52(2):468-472,5.基金项目
黑龙江省教育厅项目"智能时代基于OBE理念的C程序设计教学改革与实践"(编号:SJGY20190107) （编号:SJGY20190107）
教育部产学合作协同育人项目"校企融合下软件测试创新实践基地的建设"(编号:202002097001) （编号:202002097001）
教育部产学合作协同育人项目"校企融合背景下高校师资提升建设"(编号:202002254022)资助. （编号:202002254022）
