PIoT Malicious Traffic Detection Method Based on GAN Sample Enhancement
To solve the problem of network traffic data imbalance under the background of power Internet of things and improve the poor generalization ability of the model, a PIoT malicious traffic detection method based on GAN sample enhancement is developed. Firstly, network traffic samples are preprocessed....
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| Veröffentlicht in: | Security and communication networks 2022-03, Vol.2022, p.1-12 |
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| container_title | Security and communication networks |
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| creator | Hou, Botao Zhang, Ke Zuo, Xiaojun Zhao, Jianli Xi, Bo |
| description | To solve the problem of network traffic data imbalance under the background of power Internet of things and improve the poor generalization ability of the model, a PIoT malicious traffic detection method based on GAN sample enhancement is developed. Firstly, network traffic samples are preprocessed. Aiming at the imbalance of network traffic, malicious samples generation based on GAN is adopted, which uses the advantages of confrontation training in GAN to generate a small amount of malicious traffic to balance the PIoT malicious traffic. Secondly, 33 features are selected serially to construct a malicious traffic feature set by using analysis of variance and correlation analysis. Finally, the PIoT malicious traffic detection algorithm is implemented based on CatBoost and grid search. The effectiveness of the proposed method is verified on the public dataset CICIDS2017. The experimental results show that the recall rate of the proposed method on CatBoost reaches 96.60%, which is 21.16% higher than that before unbalancing, and the detection accuracy rate reaches 97.96%, which increases 8% compared to that of the other balanced methods, which significantly improves the detection performance of PIoT malicious traffic. |
| doi_str_mv | 10.1155/2022/9223412 |
| format | Article |
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Firstly, network traffic samples are preprocessed. Aiming at the imbalance of network traffic, malicious samples generation based on GAN is adopted, which uses the advantages of confrontation training in GAN to generate a small amount of malicious traffic to balance the PIoT malicious traffic. Secondly, 33 features are selected serially to construct a malicious traffic feature set by using analysis of variance and correlation analysis. Finally, the PIoT malicious traffic detection algorithm is implemented based on CatBoost and grid search. The effectiveness of the proposed method is verified on the public dataset CICIDS2017. The experimental results show that the recall rate of the proposed method on CatBoost reaches 96.60%, which is 21.16% higher than that before unbalancing, and the detection accuracy rate reaches 97.96%, which increases 8% compared to that of the other balanced methods, which significantly improves the detection performance of PIoT malicious traffic.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1155/2022/9223412</identifier><language>eng</language><publisher>London: Hindawi</publisher><subject>Algorithms ; Classification ; Communication ; Communications traffic ; Computer networks ; Correlation analysis ; Datasets ; Deep learning ; Denial of service attacks ; Electricity distribution ; Feature selection ; Internet of Things ; Machine learning ; Privacy ; Protocol ; Traffic models ; Variance analysis</subject><ispartof>Security and communication networks, 2022-03, Vol.2022, p.1-12</ispartof><rights>Copyright © 2022 Botao Hou et al.</rights><rights>Copyright © 2022 Botao Hou et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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| subjects | Algorithms Classification Communication Communications traffic Computer networks Correlation analysis Datasets Deep learning Denial of service attacks Electricity distribution Feature selection Internet of Things Machine learning Privacy Protocol Traffic models Variance analysis |
| title | PIoT Malicious Traffic Detection Method Based on GAN Sample Enhancement |
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