A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks

Egils Ginters (), Uga Dumpis, Laura Calvet Liñán, Miquel Angel Piera Eroles, Kawa Nazemi, Andrejs Matvejevs and Mario Ruiz Estrada
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Egils Ginters: Information Technology Institute, Riga Technology University, LV-1048 Riga, Latvia
Uga Dumpis: Department of Internal Medicine, University of Latvia, LV-1004 Riga, Latvia
Laura Calvet Liñán: Telecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, 08913 Cerdanyola del Vallès, Spain
Miquel Angel Piera Eroles: Telecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, 08913 Cerdanyola del Vallès, Spain
Kawa Nazemi: Human-Computer Interaction and Visual Analytics, Darmstadt University of Applied Sciences, 64295 Darmstadt, Germany
Andrejs Matvejevs: Institute of Applied Mathematics, Riga Technology University, LV-1048 Riga, Latvia

Mathematics, 2024, vol. 13, issue 1, 1-35

Abstract: As digitalization and artificial intelligence advance, cybersecurity threats intensify, making malware—a type of software installed without authorization to harm users—an increasingly urgent concern. Due to malware’s social and economic impacts, accurately modeling its spread has become essential. While diverse models exist for malware propagation, their selection tends to be intuitive, often overlooking the unique aspects of digital environments. Key model choices include deterministic vs. stochastic, planar vs. spatial, analytical vs. simulation-based, and compartment-based vs. individual state-tracking models. In this context, our study assesses fundamental infection spread models to determine those most applicable to malware propagation. It is organized in two parts: the first examines principles of deterministic and stochastic infection models, and the second provides a comparative analysis to evaluate model suitability. Key criteria include scalability, robustness, complexity, workload, transparency, and manageability. Using consistent initial conditions, control examples are analyzed through Python-based numerical methods and agent-based simulations in NetLogo. The findings yield practical insights and recommendations, offering valuable guidance for researchers and cybersecurity professionals in applying epidemiological models to malware spread.

Keywords: epidemiological models; mathematical modeling; malware spread modeling; sociotechnical systems; simulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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