A statistical approach to botnet virulence estimation
Bibliographic record
Abstract
Network vulnerability and infection rates are key factors in mathematical models of botnet propagation dynamics, which in turn are increasingly deemed to have potential for playing an important role in various botnet mitigation strategies. In this paper we discuss research that draws on epidemiological models in biology in order to solve the problem of how to estimate network vulnerability and infection rates in relation to a botnet. This research provides botnet propagation models with concrete measures that make those models practical, and hence employable in mitigation of real world botnets in a timely fashion. The proposed estimation approach is based on random sampling and follows a novel application of statistical learning and inference in a botnet-versus-network setting. We have implemented this research in the Matlab programming language, and thus in the paper we also discuss an experimental validation of the effectiveness of this research with respect to realistically simulated botnet propagation dynamics in a GTNetS network simulation platform.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".