Locator/Identifier Separation: Comparison and Analysis on the Mitigation of Worm Propagation
Why this work is in the frame
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Bibliographic record
Abstract
As a basic prerequisite for worm detection based on computational intelligence in networks with locator/identifier separation, it is well worth considering the influence on worm propagation due to the incoming locator/identifier separation.In this paper, according to the characteristics of locator/identifier separation, we systematically analyze the mitigation of worm propagation in three aspects: address semantics, address space and mapping delay.By applying the classical AAWP and SIR worm propagation models, we give a quantitative comparison between today's Internet and networks with locator/identifier separation.In particular, our research results show that, the characteristics of locator/identifier separation can help to markedly mitigate worm propagation, and networks with locator/identifier separation are more resistant to worm propagation than today's Internet.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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