Feature Engineering for Highly Irregular Network Traffic Prediction
Bibliographic record
Abstract
Predicting network traffic with reasonable accuracy in short-term horizons is often a challenging task due to high randomness. Network traffic data can be decomposed into a number of features creating a highly complex multivariate time series. Usually, this randomness is not equally distributed across all related time series, which decreases the predictability of the whole multivariate time series. This paper presents a novel feature engineering solution that can potentially increase prediction performance for models where the prediction horizon becomes especially short (e.g., in 5 seconds range). Specifically, we propose two feature extraction techniques based on a) IP changes and b) flow direction flips. We compare the predictability performance of our proposed approaches with the baseline scheme (i.e., no feature extraction). The comparison was performed on a network traffic dataset collected from commercial live networks. Although the proposed approaches capture the specifics of the provided dataset, the outcomes of our research can be generalized to any encrypted network traffic. Our results show improvements in MAPE metrics and up to a 93% reduction in computation time with our proposed approaches.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".