Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues
The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particular...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2368 |
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| author | Changqing Zhao Ling Xia Liao Guomin Chen Han-Chieh Chao |
| author_facet | Changqing Zhao Ling Xia Liao Guomin Chen Han-Chieh Chao |
| author_sort | Changqing Zhao |
| collection | DOAJ |
| description | The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data. This paper explores data and knowledge condensation methods—such as coreset selection, data compression, knowledge distillation, and dataset distillation—within the context of traffic classification tasks. It clarifies the relationship between these techniques and network traffic classification, introducing each method and its typical applications. This paper also outlines potential scenarios for applying each condensation technique, highlighting the associated challenges and open research issues. To the best of our knowledge, this is the first comprehensive summary of condensation techniques specifically tailored for network traffic classification tasks. |
| format | Article |
| id | doaj-art-9bb9c215f5f441a4a25685ea44e42cf5 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9bb9c215f5f441a4a25685ea44e42cf52025-08-20T03:13:59ZengMDPI AGSensors1424-82202025-04-01258236810.3390/s25082368Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open IssuesChangqing Zhao0Ling Xia Liao1Guomin Chen2Han-Chieh Chao3School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, ChinaSchool of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, ChinaSchool of Management, Guilin University of Aerospace Technology, Guilin 541004, ChinaDepartment of Applied Informatics, Fo Guang University, Yilan 36247, TaiwanThe accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data. This paper explores data and knowledge condensation methods—such as coreset selection, data compression, knowledge distillation, and dataset distillation—within the context of traffic classification tasks. It clarifies the relationship between these techniques and network traffic classification, introducing each method and its typical applications. This paper also outlines potential scenarios for applying each condensation technique, highlighting the associated challenges and open research issues. To the best of our knowledge, this is the first comprehensive summary of condensation techniques specifically tailored for network traffic classification tasks.https://www.mdpi.com/1424-8220/25/8/2368network traffic classificationconcept driftknowledge transferringmodel distillationdataset distillation |
| spellingShingle | Changqing Zhao Ling Xia Liao Guomin Chen Han-Chieh Chao Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues Sensors network traffic classification concept drift knowledge transferring model distillation dataset distillation |
| title | Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues |
| title_full | Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues |
| title_fullStr | Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues |
| title_full_unstemmed | Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues |
| title_short | Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues |
| title_sort | condensation of data and knowledge for network traffic classification techniques applications and open issues |
| topic | network traffic classification concept drift knowledge transferring model distillation dataset distillation |
| url | https://www.mdpi.com/1424-8220/25/8/2368 |
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