Statistical methods for computer network traffic analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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