Out-Of-Distribution Is Not Magic: The Clash Between Rejection Rate and Model Success
Bibliographic record
Abstract
Recent advancements in Internet protocols, including DNS over HTTPS (DoH) and Encrypted Service Name Indicators (ESNI), are making traditional Deep Packet Inspection (DPI) engines obsolete.Consequently, there is a growing need for nextgeneration traffic classification using artificial intelligence (AI).While DPI automatically categorizes unknown traffic as 'other,' AI-based models cannot automatically handle unknown or Outof-Distribution (OOD) traffic.AI models must effectively detect and classify OOD traffic to ensure robustness, reliability, and accuracy in real-world applications; however, current research often fails to address the challenges of OOD detection.In this paper, we evaluate various state-of-the-art OOD detection techniques for internet traffic classification and explore the drawbacks and advantages of using different threshold levels for the model's tolerance for OOD.Our findings reveal that varying rejection rates have distinct effects on OOD techniques, leading to a change in the optimal strategy for achieving dependable and precise detection across diverse OOD scenarios.We demonstrate that adjusting rejection rates from 10% to 30% can significantly improve the True Detection Rate (TDR) by up to 50%, while the False Detection Rate (FDR) may increase by less than 10%.Moreover, we emphasize that rejection-rate-based evaluation is pivotal for next-generation flow classification, promising a substantial reduction in FDR through rigorous methodological assessment.
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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 | 0.088 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".