MS-PreTE: A Multi-Scale Pre-Training Encoder for Mobile Encrypted Traffic Classification
Shranjeno v:
| izdano v: | Big Data and Cognitive Computing vol. 9, no. 8 (2025), p. 216-238 |
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| Glavni avtor: | |
| Drugi avtorji: | , , , |
| Izdano: |
MDPI AG
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| Teme: | |
| Online dostop: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Oznake: |
Brez oznak, prvi označite!
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| 001 | 3243981581 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-2289 | ||
| 024 | 7 | |a 10.3390/bdcc9080216 |2 doi | |
| 035 | |a 3243981581 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Wang, Ziqi |u Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; ziqiwang00@foxmail.com (Z.W.); liuxinyi@e.gzhu.edu.cn (X.L.) | |
| 245 | 1 | |a MS-PreTE: A Multi-Scale Pre-Training Encoder for Mobile Encrypted Traffic Classification | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Mobile traffic classification serves as a fundamental component in network security systems. In recent years, pre-training methods have significantly advanced this field. However, as mobile traffic is typically mixed with third-party services, the deep integration of such shared services results in highly similar TCP flow characteristics across different applications. This makes it challenging for existing traffic classification methods to effectively identify mobile traffic. To address the challenge, we propose MS-PreTE, a two-phase pre-training framework for mobile traffic classification. MS-PreTE introduces a novel multi-level representation model to preserve traffic information from diverse perspectives and hierarchical levels. Furthermore, MS-PreTE incorporates a focal-attention mechanism to enhance the model’s capability in discerning subtle differences among similar traffic flows. Evaluations demonstrate that MS-PreTE achieves state-of-the-art performance on three mobile application datasets, boosting the F1 score for Cross-platform (iOS) to 99.34% (up by 2.1%), Cross-platform (Android) to 98.61% (up by 1.6%), and NUDT-Mobile-Traffic to 87.70% (up by 2.47%). Moreover, MS-PreTE exhibits strong generalization capabilities across four real-world traffic datasets. | |
| 653 | |a Traffic flow | ||
| 653 | |a Machine learning | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Applications programs | ||
| 653 | |a Security systems | ||
| 653 | |a Mobile communications networks | ||
| 653 | |a Neural networks | ||
| 653 | |a Mobile computing | ||
| 653 | |a Architecture | ||
| 653 | |a Flow characteristics | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Qiu Yufan |u Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; ziqiwang00@foxmail.com (Z.W.); liuxinyi@e.gzhu.edu.cn (X.L.) | |
| 700 | 1 | |a Liu, Yaping |u Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; ziqiwang00@foxmail.com (Z.W.); liuxinyi@e.gzhu.edu.cn (X.L.) | |
| 700 | 1 | |a Zhang, Shuo |u Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; ziqiwang00@foxmail.com (Z.W.); liuxinyi@e.gzhu.edu.cn (X.L.) | |
| 700 | 1 | |a Liu, Xinyi |u Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; ziqiwang00@foxmail.com (Z.W.); liuxinyi@e.gzhu.edu.cn (X.L.) | |
| 773 | 0 | |t Big Data and Cognitive Computing |g vol. 9, no. 8 (2025), p. 216-238 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243981581/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243981581/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243981581/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |