GMTBLC: a deep learning-based bi-modal network traffic classification method
Network traffic classification is crucial for network security maintenance and management, and it has been widely applied in tasks, such as quality of service (QoS) assurance and intrusion detection. To address the issues of traditional traffic classification models, such as insufficient feature ext...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-12-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024251/ |
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| author | WEI Debin JIANG Qinlong WEN Jinglong WANG Xinrui |
| author_facet | WEI Debin JIANG Qinlong WEN Jinglong WANG Xinrui |
| author_sort | WEI Debin |
| collection | DOAJ |
| description | Network traffic classification is crucial for network security maintenance and management, and it has been widely applied in tasks, such as quality of service (QoS) assurance and intrusion detection. To address the issues of traditional traffic classification models, such as insufficient feature extraction and low classification accuracy, a dual-modal network traffic classification method based on group mix attention (GMA) with a transformer and a bi-directional long short-term memory (Bi-LSTM) network, named group mix transformer and Bi-LSTM for traffic classification (GMTBLC), was proposed. In the data preprocessing phase, packet-level images within sessions were generated from the payloads of data packets to reduce information interference. In the classification phase, the images were firstly processed by the packet group mix transformer (PCMT) module, which utilized the transformer and GMA to capture global features. Simultaneously, session images were processed by the spatio-temporal feature extraction (SFE) module, of which the spatial features of packets were extracted by a convolutional neural network with residual connections, and temporal features of packets were extracted by a bi-directional long short-term memory network. In the fusion classification layer, the above global and spatiotemporal features were integrated using a dynamic weighting mechanism to complete network traffic classification. Experimental results on ISCX and USTC-TFC2016 datasets demonstrate that the proposed model achieves a classification accuracy of 99.31%, with precision, recall, and F1-score all above 98%, and outperforms the other models in classification effectiveness. |
| format | Article |
| id | doaj-art-3aaae58a3b824824af0638c298f7e8bd |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-3aaae58a3b824824af0638c298f7e8bd2025-01-15T03:34:21ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-12-01409310679426094GMTBLC: a deep learning-based bi-modal network traffic classification methodWEI DebinJIANG QinlongWEN JinglongWANG XinruiNetwork traffic classification is crucial for network security maintenance and management, and it has been widely applied in tasks, such as quality of service (QoS) assurance and intrusion detection. To address the issues of traditional traffic classification models, such as insufficient feature extraction and low classification accuracy, a dual-modal network traffic classification method based on group mix attention (GMA) with a transformer and a bi-directional long short-term memory (Bi-LSTM) network, named group mix transformer and Bi-LSTM for traffic classification (GMTBLC), was proposed. In the data preprocessing phase, packet-level images within sessions were generated from the payloads of data packets to reduce information interference. In the classification phase, the images were firstly processed by the packet group mix transformer (PCMT) module, which utilized the transformer and GMA to capture global features. Simultaneously, session images were processed by the spatio-temporal feature extraction (SFE) module, of which the spatial features of packets were extracted by a convolutional neural network with residual connections, and temporal features of packets were extracted by a bi-directional long short-term memory network. In the fusion classification layer, the above global and spatiotemporal features were integrated using a dynamic weighting mechanism to complete network traffic classification. Experimental results on ISCX and USTC-TFC2016 datasets demonstrate that the proposed model achieves a classification accuracy of 99.31%, with precision, recall, and F1-score all above 98%, and outperforms the other models in classification effectiveness.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024251/traffic classificationdeep learningattention mechanismTransformerLSTM |
| spellingShingle | WEI Debin JIANG Qinlong WEN Jinglong WANG Xinrui GMTBLC: a deep learning-based bi-modal network traffic classification method Dianxin kexue traffic classification deep learning attention mechanism Transformer LSTM |
| title | GMTBLC: a deep learning-based bi-modal network traffic classification method |
| title_full | GMTBLC: a deep learning-based bi-modal network traffic classification method |
| title_fullStr | GMTBLC: a deep learning-based bi-modal network traffic classification method |
| title_full_unstemmed | GMTBLC: a deep learning-based bi-modal network traffic classification method |
| title_short | GMTBLC: a deep learning-based bi-modal network traffic classification method |
| title_sort | gmtblc a deep learning based bi modal network traffic classification method |
| topic | traffic classification deep learning attention mechanism Transformer LSTM |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024251/ |
| work_keys_str_mv | AT weidebin gmtblcadeeplearningbasedbimodalnetworktrafficclassificationmethod AT jiangqinlong gmtblcadeeplearningbasedbimodalnetworktrafficclassificationmethod AT wenjinglong gmtblcadeeplearningbasedbimodalnetworktrafficclassificationmethod AT wangxinrui gmtblcadeeplearningbasedbimodalnetworktrafficclassificationmethod |