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MS-PreTE: A Multi-Scale Pre-Training Encoder for Mobile Encrypted Traffic Classification

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Vydáno v:Big Data and Cognitive Computing vol. 9, no. 8 (2025), p. 216-238
Hlavní autor: Wang, Ziqi
Další autoři: Qiu Yufan, Liu, Yaping, Zhang, Shuo, Liu, Xinyi
Vydáno:
MDPI AG
Témata:
Traffic flow
Machine learning
Datasets
Deep learning
Classification
Applications programs
Security systems
Mobile communications networks
Neural networks
Mobile computing
Architecture
Flow characteristics
Semantics
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Abstrakt: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.
ISSN:2504-2289
DOI:10.3390/bdcc9080216
Zdroj:Advanced Technologies & Aerospace Database