Controlling False Alarm/Discovery Rates in Online Internet Traffic Flow Classification
Why this work is in the frame
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Bibliographic record
Abstract
Existing Internet traffic classification techniques achieve impressively low misclassification rates, but do not provide performance guarantees for particular classes of interest. In this paper, we propose two novel online traffic classifiers - one based on Neyman-Pearson classification and one based on the Learning Satisfiability (LSAT) framework - that can provide class-specific performance guarantees on the false alarm and false discovery rates, respectively. We also present a preprocessor for our classifiers that predicts, after the reception of only a small number of packets, whether a flow will be 'large' (as defined by a network operator). Only these resource-intensive flows are passed to the classifier, greatly reducing the computation burden imposed. We validate our methodology by testing our approaches using traffic data provided by an ISP.
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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 | 0.001 |
| Open science | 0.001 | 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 it