A Longitudinal Study of P2P Traffic Classification
Bibliographic record
Abstract
This paper focuses on network traffic measurement of Peer-to-Peer (P2P) applications on the Internet. P2P applications supposedly constitute a substantial proportion of today's Internet traffic. However, current P2P applications use several obfuscation techniques, including dynamic port numbers, port hopping, HTTP masquerading, chunked file transfers, and encrypted payloads. As P2P applications continue to evolve, robust and effective methods are needed for P2P traffic identification. The paper compares three methods to classify P2P applications: port-based classification, application-layer signatures, and transport-layer analysis. The study uses empirical network traces collected from the University of Calgary Internet connection for the past 2 years. The results show that port-based analysis is ineffective, being unable to identify 30%-70 % of today's Internet traffic. Application signatures are accurate, but may not be possible for legal or technical reasons. The transport-layer method seems promising, providing a robust means to assess aggregate P2P traffic. The latter method suggests that 30%-70 % of the campus Internet traffic for the past year was P2P.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".