Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network

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Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network

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[1]ZHANG Anlin,ZHANG Qikun,HUANG Daoying,et al.Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network[J].Journal of Zhengzhou University (Engineering Science),2022,43(03):37-43.[doi:10.13705/j.issn.1671-6833.2022.03.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 03 Page number: 37-43 Column: Public date: 2022-04-10

Title:: Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network

Author(s):: ZHANG Anlin1; ZHANG Qikun2; HUANG Daoying2; LIU Jianghao2; LI Jianchun2; CHEN Xiaowen2; 1.Engineering Training Center, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
2.College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China

Keywords:: intrusion detection; convolutional neural networks; bidirectional gated recurrent unit; synthetic minority over-sampling technique algorithm; Tomek Links algorithm

CLC:: TP393；TP183

DOI:: 10.13705/j.issn.1671-6833.2022.03.003

Abstract:: Aiming at the problems of unbalanced data types and incomplete feature learning in deep learning intrusion detection, a neural network intrusion detection model based on the fusion of convolutional neural networks(CNN)and bidirectional gated recurrent unit(BiGRU)was proposed.The SMOTE-Tomek algorithm was used to balance the data set, the feature importance algorithm based on mean decrease impurity was used to realize feature selection; the CNN and BiGRU models used for feature fusion and attention mechanism was introduced for feature extraction, so as to improve the overall detection performance of the model.The intrusion detection data set CSE-CIC-IDS2018 was used for multi classification experiments, the model was compared with the classical single deep learning models.The experimental results showed that, firstly, in terms of data set balance, after being processed by SMOTE-Tomek algorithm, the recognition accuracy of DoS attacks-Slow HTTP Test class was improved from 0 to 34.66%, that of SQL Injection class was improved from 0 to 100%, and DDoS attack-LOIC-UDP, Brute Force-Web and Brute Force-XSS classes were improved by 5.22 percentage points, 6.55 percentage points and 35.71 percentage points respectively.It was proved that the balanced data set improved the recognition accuracy of a few classes significantly compared with the unprocessed data set.Secondly, in terms of the overall detection performance of the model, in the comparison of multi classification experiments, the overall classification accuracy, recall and F1 value of the model in this study were higher than those of several other single neural network models.The overall evaluation accuracy of each attack traffic category was about 2.10 percentage points higher than that of the highest LSTM model.The recall rate of the overall evaluation was about 1.50 percentage points higher than that of the highest LSTM model.Compared with the highest GRU model, the overall F1 value increased by about 1.97 percentage points.It was proved that the model had better detection effect.

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Last Update: 2022-05-02