Bibliographic record
Abstract
Rapidly-spreading malicious software is an important threat on today's computer networks. Most solutions that have been proposed to counter this threat are based on our ability to quickly detect the malware-generated traffic or the malware instances themselves, something that in many cases can be beyond our ability. Nonetheless, it seems intuitive that certain defensive postures adopted in configuring networks or machines can have a positive impact on countering malware, regardless of our ability to detect it. It is thus important to quantitatively understand how changes in design and deployment strategies can affect malware performance; only then does it become possible to make optimal decisions. To that purpose, we study in this paper the impact of network interconnection topologies on the propagation of malware. We first use a theoretical model based on Markov processes to try to predict the progression of an infection under varying interconnection scenarios. We then compare these predictions with experimental results obtained by launching a malware emulation agent on three differently configured networks. Both theoretical and experimental results provide quantitative confirmation of the intuition that networks with higher degrees of interconnection allow faster spread of malware. In addition to this, we believe that the models, experimental methodology and tools described here can be safely and fruitfully used to study other aspects of malware performance, and hence of the relative effectiveness of defensive counter-measures.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".