Towards In-Network Drift-Aware Traffic Classification
Bibliographic record
Abstract
Recent advances in intelligent data-plane designs have enabled efficient inference of machine learning models on programmable switches using P4. However, existing solutions focus primarily on classifying traffic that belongs to previously observed classes, i.e., in-distribution traffic, and often overlook the impact of concept drift, i.e., the emergence of out-of-distribution traffic. In this paper, we propose an approach that not only accurately classifies in-distribution traffic but also detects drifting samples that deviate from known classes. Our approach leverages a triplet network to learn an encoder that maps traffic input features to a latent space where representations of the same class form compact clusters. Drifting samples are identified based on their distance to class centroids in the latent space. Experimental results on two use cases demonstrate that our method achieves superior drifting sample detection performance compared to confidence-based schemes, while maintaining comparable in-distribution classification accuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 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".