A Survey of SIR ‐Based Differential Epidemic Models for Control and Security Against Malware Propagation in Computer Networks

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.