An Effective Intrusion Detection System Based on the FSA-BGRU Hybrid Model  

作　　者：Deng Zaihui Li Zihang Guo Jianzhong Gan Guangming Kong Dejin 

机构地区：[1]School of Electronic and Electrical Engineering,Wuhan Textile University,Wuhan 430200,China [2]School of Computer Science and Artificial Intelligence,Wuhan Textile University,Wuhan 430200,China [3]School of Physics and Electronic Information Engineering,Hubei Engineering University,Xiaogan 432000,China

出　　处：《China Communications》2025年第2期188-198,共11页中国通信(英文版)

基　　金：supported in part by the National Natural Science Foundation of China(No.62001333);the Scientific Research Project of Education Department of Hubei Province(No.D20221702).

摘　　要：Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.

关 键 词：bidirectional GRU feature selection intrusion detection system multilayer perceptron recursive feature elimination support vector machine 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]