Network Intrusion Detection Model Based on CNN and GRU
A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4184 |
| _version_ | 1797505752368152576 |
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| author | Bo Cao Chenghai Li Yafei Song Yueyi Qin Chen Chen |
| author_facet | Bo Cao Chenghai Li Yafei Song Yueyi Qin Chen Chen |
| author_sort | Bo Cao |
| collection | DOAJ |
| description | A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy. Then, the spatial features are extracted by using a convolutional neural network, and further extracted by fusing Averagepooling and Maxpooling, using attention mechanism to assign different weights to the features, thus reducing the overhead and improving the model performance. At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, a softmax function is used for classification. The proposed intrusion detection model is evaluated based on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 datasets, and the experimental results show that the classification accuracy reaches 86.25%, 99.69%, 99.65%, which are 1.95%, 0.47% and 0.12% higher than that of the same type of CNN-GRU, and can solve the problems of low classification accuracy and class imbalance well. |
| first_indexed | 2024-03-10T04:22:40Z |
| format | Article |
| id | doaj.art-310e0902ff4c4bec802a56e9aa096ede |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:40Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-310e0902ff4c4bec802a56e9aa096ede2023-11-23T07:45:27ZengMDPI AGApplied Sciences2076-34172022-04-01129418410.3390/app12094184Network Intrusion Detection Model Based on CNN and GRUBo Cao0Chenghai Li1Yafei Song2Yueyi Qin3Chen Chen4College of Air and Missile Defense, Air Force Engineering University, Xi’an 710051, ChinaCollege of Air and Missile Defense, Air Force Engineering University, Xi’an 710051, ChinaCollege of Air and Missile Defense, Air Force Engineering University, Xi’an 710051, ChinaCollege of Computer, Chang’an University, Xi’an 710061, ChinaCollege of Air and Missile Defense, Air Force Engineering University, Xi’an 710051, ChinaA network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy. Then, the spatial features are extracted by using a convolutional neural network, and further extracted by fusing Averagepooling and Maxpooling, using attention mechanism to assign different weights to the features, thus reducing the overhead and improving the model performance. At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, a softmax function is used for classification. The proposed intrusion detection model is evaluated based on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 datasets, and the experimental results show that the classification accuracy reaches 86.25%, 99.69%, 99.65%, which are 1.95%, 0.47% and 0.12% higher than that of the same type of CNN-GRU, and can solve the problems of low classification accuracy and class imbalance well.https://www.mdpi.com/2076-3417/12/9/4184convolutional neural networkgated recurrent unitintrusion detectiondata balancingfeature selection |
| spellingShingle | Bo Cao Chenghai Li Yafei Song Yueyi Qin Chen Chen Network Intrusion Detection Model Based on CNN and GRU Applied Sciences convolutional neural network gated recurrent unit intrusion detection data balancing feature selection |
| title | Network Intrusion Detection Model Based on CNN and GRU |
| title_full | Network Intrusion Detection Model Based on CNN and GRU |
| title_fullStr | Network Intrusion Detection Model Based on CNN and GRU |
| title_full_unstemmed | Network Intrusion Detection Model Based on CNN and GRU |
| title_short | Network Intrusion Detection Model Based on CNN and GRU |
| title_sort | network intrusion detection model based on cnn and gru |
| topic | convolutional neural network gated recurrent unit intrusion detection data balancing feature selection |
| url | https://www.mdpi.com/2076-3417/12/9/4184 |
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