Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature
Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conduct...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024013/ |
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| _version_ | 1841540061675388928 |
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| author | Taotao LIU Yu FU Kun WANG Xueyuan DUAN |
| author_facet | Taotao LIU Yu FU Kun WANG Xueyuan DUAN |
| author_sort | Taotao LIU |
| collection | DOAJ |
| description | Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection. |
| format | Article |
| id | doaj-art-a16d3af3322d457db2c48a0c5c40ed5a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-a16d3af3322d457db2c48a0c5c40ed5a2025-01-14T06:22:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-02-0145546759383023Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of featureTaotao LIUYu FUKun WANGXueyuan DUANConsidering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024013/intrusion detectionnetwork trafficclass imbalancefeature selectionfusion of statistical importance |
| spellingShingle | Taotao LIU Yu FU Kun WANG Xueyuan DUAN Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature Tongxin xuebao intrusion detection network traffic class imbalance feature selection fusion of statistical importance |
| title | Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature |
| title_full | Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature |
| title_fullStr | Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature |
| title_full_unstemmed | Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature |
| title_short | Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature |
| title_sort | network intrusion detection method based on vae cwgan and fusion of statistical importance of feature |
| topic | intrusion detection network traffic class imbalance feature selection fusion of statistical importance |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024013/ |
| work_keys_str_mv | AT taotaoliu networkintrusiondetectionmethodbasedonvaecwganandfusionofstatisticalimportanceoffeature AT yufu networkintrusiondetectionmethodbasedonvaecwganandfusionofstatisticalimportanceoffeature AT kunwang networkintrusiondetectionmethodbasedonvaecwganandfusionofstatisticalimportanceoffeature AT xueyuanduan networkintrusiondetectionmethodbasedonvaecwganandfusionofstatisticalimportanceoffeature |