Integrating Machine Learning with Off-the-Shelf Traffic Flow Features for HTTP/HTTPS Traffic Classification
Bibliographic record
Abstract
Accurate traffic classification is a key requirement for different network and security monitoring/planning tools. The evolution of Internet protocols and applications has caused traditional traffic classification approaches to be ineffective in certain cases. Key causes of the inaccuracy include: (i) the increase in the encrypted traffic; (ii) the rise in the usage of dynamic port numbers for different applications; and (iii) multiple applications running over HTTP/HTTPS protocols. Traditional solutions for traffic analysis, classification, and measurement fall short in providing visibility in users' activities - a key requirement for network and security monitoring tools. In this paper, we evaluate an automatic classifier for encrypted Social media, Video and Audio traffic without relying on particular application layer header fields that can be easily modified. We leverage machine learning algorithms together with the features provided by the well-known off-the-shelf traffic flow exporters. We evaluate the performance of such a system also for generalization (robustness) purposes on different networks. Experimental results show promising performances in terms of generating robust traffic classification on large traffic data when the trained model is moved to different networks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".