FSTC: Dynamic Category Adaptation for Encrypted Network Traffic Classification
Bibliographic record
Abstract
With the advancement in security and privacy on the Internet, network traffic has become increasingly difficult to classify. Current deep learning (DL)-based encrypted network traffic classification approaches rely on protocol-specific features (e.g., TLS headers) and/or assume that the classification categories (i.e., applications) remain constant over time. However, both the encryption protocols and applications continue to evolve. Therefore, DL models must be retrained from scratch for newer encryption protocols or applications, which makes existing approaches intractable in practice. In this paper, we propose novel Transfer Learning (TL) approaches for introducing new traffic classes to DL models without retraining them from scratch. We also propose a framework named FSTC, which leverages Active Learning (AL) to achieve human-assisted TL for new traffic classes and minimizes the labeled data needed for encrypted network traffic classification. We evaluate our TL and AL approaches using protocol-agnostic features from the publicly available ISCXVPN2016 and QUIC datasets. To the best of our knowledge, neither proposal has been explored before in the existing literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".