Analyzing the accuracy of CHOKe hits, CHOKe misses and CHOKe-RED drops
Bibliographic record
Abstract
CHOKe, xCHOKe and RECHOKe are preferential dropping schemes that have been proposed for detection, control and punishment of malicious flows at routers in IP networks. They use CHOKe hits, CHOKe misses and/or CHOKe-RED drops to carry out these tasks. In this paper we investigate the accuracy of malicious flow detection by using these hits, misses and drops (using ns-2). We also point out the unreliability of CHOKe hits and misses, when compared to CHOKe-RED drops, as they affect TCP-friendly flows adversely. By doing so, we present two variations of CHOKe called Half1 and Half2 to improve CHOKe and compare them with CHOKe. Half1 and Half2 outperform CHOKe when the combined rates of malicious flows are less or greater than the link capacity respectively.
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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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".