Survey of research on encrypted traffic classification based on machine learning
Encrypted traffic classification was an important component of network management and security protection. However, the complexity and variability of the current network traffic environment rendered traditional classification methods largely ineffective. Machine learning, particularly deep learning,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025006/ |
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| _version_ | 1849721407463751680 |
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| author | FU Yu LIU Taotao WANG Kun YU Yihan |
| author_facet | FU Yu LIU Taotao WANG Kun YU Yihan |
| author_sort | FU Yu |
| collection | DOAJ |
| description | Encrypted traffic classification was an important component of network management and security protection. However, the complexity and variability of the current network traffic environment rendered traditional classification methods largely ineffective. Machine learning, particularly deep learning, with its strong feature extraction capabilities, has been widely used in the field of encrypted traffic classification. To this end, a systematic review of the latest advancements in machine learning-driven encrypted traffic classification was provided. Firstly, the encrypted traffic classification work was roughly divided into three parts: data collection and processing, feature extraction and selection, and traffic classification and performance evaluation, which correspond to data acquisition, significant feature construction, and model application and validation in encrypted traffic classification. The content was further subdivided into seven stages: traffic collection, dataset construction, data preprocessing, feature extraction, feature selection, classification models, and performance evaluation. A comprehensive summary, synthesis, and analysis of these seven stages were then conducted. Finally, the challenges faced by current research were analyzed in detail, and the future research directions for encrypted traffic classification were prospected. |
| format | Article |
| id | doaj-art-e2157cb9247a4e3ead9e737d13c95054 |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-e2157cb9247a4e3ead9e737d13c950542025-08-20T03:11:40ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-01-014616719182296237Survey of research on encrypted traffic classification based on machine learningFU YuLIU TaotaoWANG KunYU YihanEncrypted traffic classification was an important component of network management and security protection. However, the complexity and variability of the current network traffic environment rendered traditional classification methods largely ineffective. Machine learning, particularly deep learning, with its strong feature extraction capabilities, has been widely used in the field of encrypted traffic classification. To this end, a systematic review of the latest advancements in machine learning-driven encrypted traffic classification was provided. Firstly, the encrypted traffic classification work was roughly divided into three parts: data collection and processing, feature extraction and selection, and traffic classification and performance evaluation, which correspond to data acquisition, significant feature construction, and model application and validation in encrypted traffic classification. The content was further subdivided into seven stages: traffic collection, dataset construction, data preprocessing, feature extraction, feature selection, classification models, and performance evaluation. A comprehensive summary, synthesis, and analysis of these seven stages were then conducted. Finally, the challenges faced by current research were analyzed in detail, and the future research directions for encrypted traffic classification were prospected.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025006/traffic analysisencrypted traffic classificationmachine learningdeep learning |
| spellingShingle | FU Yu LIU Taotao WANG Kun YU Yihan Survey of research on encrypted traffic classification based on machine learning Tongxin xuebao traffic analysis encrypted traffic classification machine learning deep learning |
| title | Survey of research on encrypted traffic classification based on machine learning |
| title_full | Survey of research on encrypted traffic classification based on machine learning |
| title_fullStr | Survey of research on encrypted traffic classification based on machine learning |
| title_full_unstemmed | Survey of research on encrypted traffic classification based on machine learning |
| title_short | Survey of research on encrypted traffic classification based on machine learning |
| title_sort | survey of research on encrypted traffic classification based on machine learning |
| topic | traffic analysis encrypted traffic classification machine learning deep learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025006/ |
| work_keys_str_mv | AT fuyu surveyofresearchonencryptedtrafficclassificationbasedonmachinelearning AT liutaotao surveyofresearchonencryptedtrafficclassificationbasedonmachinelearning AT wangkun surveyofresearchonencryptedtrafficclassificationbasedonmachinelearning AT yuyihan surveyofresearchonencryptedtrafficclassificationbasedonmachinelearning |