Encrypted traffic classification method based on parallel traffic graph and graph neural network
Aiming at the problems of traditional encrypted traffic classification methods limited by the imbalance of dataset classes and the unreliability of the features used in complex network environments, an encrypted traffic classification method based on parallel traffic graph and graph neural network w...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025095/ |
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| _version_ | 1849709243252342784 |
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| author | LIU Taotao FU Yu YU Yihan AN Yishuai |
| author_facet | LIU Taotao FU Yu YU Yihan AN Yishuai |
| author_sort | LIU Taotao |
| collection | DOAJ |
| description | Aiming at the problems of traditional encrypted traffic classification methods limited by the imbalance of dataset classes and the unreliability of the features used in complex network environments, an encrypted traffic classification method based on parallel traffic graph and graph neural network was proposed. Firstly, the traffic graphs were constructed from the packet header and payload perspectives to emphasize their differences. Then, an improved graph attention network was introduced to extract effective information from the parallel traffic graphs. Next, a feature cross-fusion attention module was used to fuse the extracted information, achieving a more robust feature representation. Finally, classification was performed using fully connected layers and a Softmax layer. Experiments show that the proposed method achieves better results on the ISCX-VPN, ISCX-nonVPN, ISCX-Tor, and ISCX-nonTor datasets, with accuracies of 96.88%, 90.62%, 99.24%, and 98.13%, respectively, significantly enhancing encrypted traffic classification performance. |
| format | Article |
| id | doaj-art-5cbb76a39d5145ddbc56b111c23d25ee |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5cbb76a39d5145ddbc56b111c23d25ee2025-08-20T03:15:22ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-06-01464559114257081Encrypted traffic classification method based on parallel traffic graph and graph neural networkLIU TaotaoFU YuYU YihanAN YishuaiAiming at the problems of traditional encrypted traffic classification methods limited by the imbalance of dataset classes and the unreliability of the features used in complex network environments, an encrypted traffic classification method based on parallel traffic graph and graph neural network was proposed. Firstly, the traffic graphs were constructed from the packet header and payload perspectives to emphasize their differences. Then, an improved graph attention network was introduced to extract effective information from the parallel traffic graphs. Next, a feature cross-fusion attention module was used to fuse the extracted information, achieving a more robust feature representation. Finally, classification was performed using fully connected layers and a Softmax layer. Experiments show that the proposed method achieves better results on the ISCX-VPN, ISCX-nonVPN, ISCX-Tor, and ISCX-nonTor datasets, with accuracies of 96.88%, 90.62%, 99.24%, and 98.13%, respectively, significantly enhancing encrypted traffic classification performance.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025095/encrypted traffic classificationdeep learninggraph neural networkfeature fusion |
| spellingShingle | LIU Taotao FU Yu YU Yihan AN Yishuai Encrypted traffic classification method based on parallel traffic graph and graph neural network Tongxin xuebao encrypted traffic classification deep learning graph neural network feature fusion |
| title | Encrypted traffic classification method based on parallel traffic graph and graph neural network |
| title_full | Encrypted traffic classification method based on parallel traffic graph and graph neural network |
| title_fullStr | Encrypted traffic classification method based on parallel traffic graph and graph neural network |
| title_full_unstemmed | Encrypted traffic classification method based on parallel traffic graph and graph neural network |
| title_short | Encrypted traffic classification method based on parallel traffic graph and graph neural network |
| title_sort | encrypted traffic classification method based on parallel traffic graph and graph neural network |
| topic | encrypted traffic classification deep learning graph neural network feature fusion |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025095/ |
| work_keys_str_mv | AT liutaotao encryptedtrafficclassificationmethodbasedonparalleltrafficgraphandgraphneuralnetwork AT fuyu encryptedtrafficclassificationmethodbasedonparalleltrafficgraphandgraphneuralnetwork AT yuyihan encryptedtrafficclassificationmethodbasedonparalleltrafficgraphandgraphneuralnetwork AT anyishuai encryptedtrafficclassificationmethodbasedonparalleltrafficgraphandgraphneuralnetwork |