Encrypted Network Traffic Classification using Self-supervised Learning
Bibliographic record
Abstract
Network traffic classification is used in many applications including network provisioning, malware detection, resource management, and so on. In modern networks, use of encrypted protocols is a norm rather than an exception. Existing network traffic classification techniques fall short in working with encrypted traffic. Although deep learning based techniques have been shown to perform well in the case of encrypted traffic classification, they require an abundance of labeled data to achieve high accuracy. However, labeled data is rarely available in sufficient volumes in real network settings as they require domain experts to annotate data with labels. Therefore, in this paper, we propose a self-supervised approach that can achieve high accuracy on encrypted network traffic classification with a few labeled data. The proposed method is evaluated on three publicly available datasets. The empirical result shows that our method not only achieves high accuracy on encrypted traffic but also has the ability to apply the acquired knowledge on a different dataset. In our experiments, our method outperforms the state-of-the-art baseline methods by ~3% in terms of accuracy even with a much lower volume of labeled data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".