Modeling host-based detection and active worm containment
Bibliographic record
Abstract
Recent advancements in Internet worms propagation techniques has generated interest in the development of appropriate defense techniques against such worms. Modeling the behaviour of worm defense techniques to better understand and measure their defense capabilities is crucial to developing effective defenses. This paper presents a discrete-time model of our earlier proposed host-based worm detection and collaborative network containment defense technique, which we referred to as the Analytical Active Worm Containment (AAWC) model. The AAWC model captures the protection capability of the proposed technique by modeling the host population protected from fast spreading, scanning intrusion attack such as worms in a large scale network. Analysing the model alongside an existing discrete-time worm propagation model, we demonstrate quantitatively the effectiveness of our proposed detection and containment technique in defending against fast spreading scanning worms. Based on the host-based worm detection technique, we also develop a continuous-time probability model for worm detection interval which uniquely captures the relationship between worm scanning rate and the detection interval of the worm. Further, we investigate the introduction of immunization to our containment technique and show the resultant effect on a vulnerable population under attack using the developed model
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".