Peer-to-Peer Traffic Identification by Mining IP Layer Data Streams Using Concept-Adapting Very Fast Decision Tree
Bibliographic record
Abstract
We apply streaming data mining techniques, and in particular, concept-adapting very fast decision tree (CVFDT) to identify peer-to-peer (P2P) applications in Internet traffic, as the Internet data flows dynamically in large volumes (streaming data), and in P2P applications, new communities of peers often attend and old communities of peers often leave, requiring the identification methods to be capable of coping with concept drift, and updating the model incrementally. We captured Internet traffic at a main gateway router, performed pre-processing on the captured data, selected the most significant attributes, and prepared a training data stream to which the CVFDT model was applied. We tested our approach on a data stream with 3.5 million P2P and NonP2P traffic records. The results show that our approach can effectively deal with dynamic nature of streaming data and detect the changes in communities of peers. The classification accuracy is higher than 95%, and the method is well-scalable in both time and space complexities, making it competent for large-scale dynamic data. We extracted attributes only from the IP layer, eliminating the privacy concern associated with the techniques that use deep packet inspection.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".