Peer-to-Peer IP Traffic Classification Using Decision Tree and IP Layer Attributes
Bibliographic record
Abstract
We present a new approach using data-mining technique and, in particular, decision tree to classify peer-to-peer (P2P) traffic in IP networks. We captured the Internet traffic at a main gateway router, performed preprocessing on the data, selected the most significant attributes, and prepared a training-data set to which the decision-tree algorithm was applied. We built several models using a combination of various attribute sets for different ratios of P2P to non-P2P traffic in the training data. We observed that the accuracy of the model increases significantly when we include the attributes “Src IP addr” and “Dst IP addr” in building the model. By detecting communities of peers, we achieved classification accuracy of higher than 98%. Consequently, we recommend that: (a) the classification must be done within the authority of the Internet service providers (ISP) in order to detect communities of peers, and (b) the decision tree needs to be frequently trained to ensure the fairness and correctness of the classification algorithm. Our approach is based only on information in the IP layer, eliminating the privacy issues associated with 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".