The integration of mathematical and epidemiological models into cybersecurity has become essential for understanding how computer viruses spread. This paper offers a detailed overview of the field’s development, beginning with classical compartmental models and progressing toward more sophisticated, network-centered approaches. While biological and digital infections share key concepts—such as host vulnerability, transmission patterns, and evolution—the paper emphasizes that direct adaptation of biological models is inadequate. Modern malware, especially fast-spreading worms and focused advanced persistent threats, demands tailored modeling techniques. The discussion outlines core models like Susceptible-Infected-Removed (SIR) and Susceptible-Infected-Susceptible (SIS), along with variants such as Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-Antidotal-Infected-Removed (SAIR). It underscores the importance of the basic reproduction number (R₀), which predicts whether a digital outbreak will continue or be contained. A key conclusion is the limitation of traditional models that assume a uniform, well-mixed population. This limitation encouraged the adoption of network-based modeling, which reflects the complex structure of actual computer systems. The paper explains how features like hubs in scale-free networks significantly influence virus spread and increase vulnerability. Agent-based modeling is identified as the most detailed and realistic method, though it requires high computational resources. The study also highlights how these models inform active cybersecurity measures, including targeted patching and network segmentation. Finally, it notes that combining epidemic modeling with cyber graphs and machine learning is paving the way for adaptive, data-driven defense systems capable of countering emerging threats in real time.
Kamaljeet Kaur (Wed,) studied this question.