Statistical methods for computer network traffic analysis
Bibliographic record
Abstract
Classical time-series analysis is concerned with data that have weak correlations, Gaussian marginals, and stationarity. Computer network traffic, on the other hand, possesses many complicated and unconventional characteristics such as self-similarity, long-range dependence, heavy-tail marginals, and non-stationarities. Accurate detection and estimation of these features are essential for performance evaluation and traffic modelling. However, the presence of two or more of these features can significantly degrade the performance of statistical estimators, therefore giving poor or incorrect estimates. A critical evaluation of several state-of-the-art statistical methods that are useful for detecting and quantifying the aforementioned properties of network traffic are presented. This is done so as to determine when these methods are most applicable. Numerous experiments are carried out to gain further insights into the strength and limitations of each method. It is found that current statistical tools for estimating the tail exponent of a heavy-tailed process with long-range dependence can produce incorrect results. Hence, we propose a simple wavelet-based method that provides a more accurate estimate of the tail exponent than current existing methods when long-range dependence is present.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".