M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913861653757952 |
|---|---|
| author | Yuan, Yali Huang, Yu Zeng, Xingjian Mei, Hantao Cheng, Guang |
| author_facet | Yuan, Yali Huang, Yu Zeng, Xingjian Mei, Hantao Cheng, Guang |
| contents | The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable detection of unknown traffic patterns. Although deep learning models show promise in controlled environments, their real-world deployment is hindered by data scarcity, concept drift, and operational constraints. This paper proposes M3S-UPD, a novel Multi-Stage Self-Supervised Unknown-aware Packet Detection framework that synergistically integrates semi-supervised learning with representation analysis. Our approach eliminates artificial segregation between classification and detection tasks through a four-phase iterative process: 1) probabilistic embedding generation, 2) clustering-based structure discovery, 3) distribution-aligned outlier identification, and 4) confidence-aware model updating. Key innovations include a self-supervised unknown detection mechanism that requires neither synthetic samples nor prior knowledge, and a continuous learning architecture that is resistant to performance degradation. Experimental results show that M3S-UPD not only outperforms existing methods on the few-shot encrypted traffic classification task, but also simultaneously achieves competitive performance on the zero-shot unknown traffic discovery task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21462 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Yuan, Yali Huang, Yu Zeng, Xingjian Mei, Hantao Cheng, Guang Cryptography and Security The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable detection of unknown traffic patterns. Although deep learning models show promise in controlled environments, their real-world deployment is hindered by data scarcity, concept drift, and operational constraints. This paper proposes M3S-UPD, a novel Multi-Stage Self-Supervised Unknown-aware Packet Detection framework that synergistically integrates semi-supervised learning with representation analysis. Our approach eliminates artificial segregation between classification and detection tasks through a four-phase iterative process: 1) probabilistic embedding generation, 2) clustering-based structure discovery, 3) distribution-aligned outlier identification, and 4) confidence-aware model updating. Key innovations include a self-supervised unknown detection mechanism that requires neither synthetic samples nor prior knowledge, and a continuous learning architecture that is resistant to performance degradation. Experimental results show that M3S-UPD not only outperforms existing methods on the few-shot encrypted traffic classification task, but also simultaneously achieves competitive performance on the zero-shot unknown traffic discovery task. |
| title | M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2505.21462 |