微型电脑应用2025，Vol.41Issue(3)：1-4,4.
基于改进GAN与改进Bi-LSTM的网络入侵检测研究
Research on Network Intrusion Detection Based on Improved GAN and Improved Bi-LSTM
摘要
Abstract
To improve the detection precision of network intrusion in the big data environment,a network intrusion detection method based on improved GAN(generative adversarial network)and improved Bi-LSTM(bi-directional long short-term mem-ory)is proposed.The method adopts GAN network with gradient penalty to handle the problem of imbalanced samples,ob-tains balanced network traffic data samples,and uses Bi-LSTM network with multi-layer convolutional layers and attention mechanism to classify the data samples,achieving network intrusion detection in the big data environment.The simulation re-sults show that the proposed method can effectively detect network intrusions of different types of attacks such as fuzzer,denial of service,and exploitation of vulnerability,which has higher detection precision.Moreover,its accuracy,precision,recall,and F1 value reach 90.28％,86.55％,93.27％,and 89.64％,respectively,with a false positive rate of 4.28％.Compared with support vector machine(SVM),random forest(RF),and residual network(ResNet)models,the proposed method has significant advantages in network intrusion detection in the big data environment,providing a reference for achieving more accu-rate network intrusion detection in the big data environment.关键词
网络入侵检测/深度学习/GAN网络/Bi-LSTM网络/注意力机制Key words
network intrusion detection/deep learning/GAN network/Bi-LSTM network/attention mechanism分类
信息技术与安全科学引用本文复制引用
李漠颖,朱子奕..基于改进GAN与改进Bi-LSTM的网络入侵检测研究[J].微型电脑应用,2025,41(3):1-4,4.基金项目
2024年度中国民办教育协会规划课题学校发展类项目(CANFZG24392) （CANFZG24392）
