A Survey of <scp>SIR</scp> ‐Based Differential Epidemic Models for Control and Security Against Malware Propagation in Computer Networks
Bibliographic record
Abstract
ABSTRACT Unarguably, malware and their variants have metamorphosed into objects of attack and cyber warfare. These issues have directed research focus to modeling infrastructural settings and infection scenarios, analyzing propagation mechanisms, and conducting studies that highlight optimized remedial measures. Most importantly, these studies aim to reduce the frequency of large‐scale attacks that cause significant losses for both individuals and business organizations. However, there is a ubiquitous application of the classical differential equation‐based Susceptible‐Infected‐Recovered (SIR) model by Kermack and McKendrick for the modeling and analysis of malware propagation in several network environments. Therefore, 143 epidemic SIR‐based models were reviewed using several parameters such as infection types, incidence rates, equilibrium and stability analyses, reproduction number/epidemic threshold, graph topology, numerical methods, and sensitivity analyses, thus answering posed research questions. Other features/issues relating to computer malware or computer networks were also identified and discussed; they include differential equations, networks, user vigilance and awareness, vertical transmission, multistate antivirus/real‐time immunization, fuzzy logic, removable storage media, and optimal control. Possible open areas include the need for real‐world malware traces and networks, application‐layer protocols, IPv6, hybrid modeling, graph neural networks, cloud migration, digital twins, and the use of awareness campaigns against cybersecurity issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".