Featuring Real-Time Imbalanced Network Traffic Classification
Bibliographic record
Abstract
Recently, imbalanced traffic classification has attracted more attention due to the fact that most internet traffic exhibits imbalance behavior. However, few works only have considered real-time imbalanced traffic classification. In this project, we propose a comparative study comprising several machine learning algorithms for nine different scenarios. We vary dataset and flow sizes following an under-sampling approach, in order to establish an objective evaluation of the best parameters for classification. The results showed that: 1) Combined with packet length, inter-arrival time and maximum segment size, features related to TCP session signalization enhance imbalanced traffic classification performances; 2) Ensemble approaches, especially Bagged Random Forest, achieve the best results for real-time imbalanced traffic classification; 3) Increasing flow sizes while reducing (to a certain level) training set sizes, enhances classification performances as we learn more about each individual instance. The best classification scenario includes 500 samples in each class with 8 packets flows.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".