Scomf and SComI botnet models: The cases of initial unhindered botnet expansion
Bibliographic record
Abstract
Botnets have become platforms to launch distributed denial-of-service attacks and coordinate massive e-mail spam campaigns, to name just a few of botnet-related nefarious activities. Apart from the wired networks, the increasingly Internet-enabled cellular wireless networks are also vulnerable to botnet attacks; a situation which motivates a thorough study of botnet expansion and the mathematical models thereof. In this paper, we propose the following two Continuous-Time Markov Chain-based models for prediction of the botnet size in the initial phase of botnet lifecycle: SComF for the case of finite number of susceptible nodes (suitable for a botnet expanding in a closed environment such as an administrative domain, or a LAN) and SComI for the case of infinite number of susceptible nodes (suitable for a botnet expanding in the larger Internet). Having access to such models would enable security experts to have reliable size estimates and therefore be able to defend against an emerging botnet with adequate resources. We derive the probability distributions for both models and provide some numerical results as well as a simulation study accompanying the numerical analysis of the SComF model using the GTNetS network simulator.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".