A Survey of <scp>SIR</scp> ‐Based Differential Epidemic Models for Control and Security Against Malware Propagation in Computer Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it