中北大学学报（自然科学版）2024，Vol.45Issue(1)：66-73,8.DOI:10.3969/j.issn.1673-3193.2024.01.009
基于多阶段特征选择和CNN-GRU的网络入侵检测模型
Network Intrusion Detection Model Based on Multi-Stage Feature Selection and CNN-GRU
王相月 1赵利辉1
作者信息
- 1. 中北大学 软件学院,山西 太原 030051
- 折叠
摘要
Abstract
A network intrusion detection model based on multi-stage feature selection and CNN-GRU is proposed to address the problem of low accuracy of intrusion detection due to redundant features of net-work intrusion detection data.Firstly,for the feature redundancy of the data set,the PCC-RF feature selection algorithm is constructed by combining Pearson correlation coefficient and random forest for multi-stage feature selection and constructing the optimal feature subset.Then the CNN-GRU model is con-structed by using the powerful extraction capability of convolutional neural network for spatial features and the excellent temporal feature extraction capability of gated recurrent units.Finally,the optimal feature subset is input into the CNN-GRU model for training.Experiments are conducted by using the UNSW-NB15 dataset,and the experimental results show that the dataset,after the PCC-RF feature processing algorithm,has lower dimensionality and better results compared with other methods.The model detection accuracy reaches 84.72%.关键词
网络入侵检测/特征选择/卷积神经网络/门控循环单元Key words
network intrusion detection/feature selection/convolutional neural network/gated cycle unit分类
信息技术与安全科学引用本文复制引用
王相月,赵利辉..基于多阶段特征选择和CNN-GRU的网络入侵检测模型[J].中北大学学报（自然科学版）,2024,45(1):66-73,8.
