Analyzing the accuracy of CHOKe hits, CHOKe misses and CHOKe-RED drops
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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