A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks
Document Type
Article
Publication Date
1-1-2025
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
Divisions
Faculty_of_Business_and_Accountancy
Publication Title
Mathematics
Volume
13
Issue
1
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND