A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks
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. W...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/91 |
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| author | Egils Ginters Uga Dumpis Laura Calvet Liñán Miquel Angel Piera Eroles Kawa Nazemi Andrejs Matvejevs Mario Arturo Ruiz Estrada |
| author_facet | Egils Ginters Uga Dumpis Laura Calvet Liñán Miquel Angel Piera Eroles Kawa Nazemi Andrejs Matvejevs Mario Arturo Ruiz Estrada |
| author_sort | Egils Ginters |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-83be151973f64e2e9b7ff0b65e8d9eb9 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-83be151973f64e2e9b7ff0b65e8d9eb92025-01-10T13:18:13ZengMDPI AGMathematics2227-73902024-12-011319110.3390/math13010091A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage OutbreaksEgils Ginters0Uga Dumpis1Laura Calvet Liñán2Miquel Angel Piera Eroles3Kawa Nazemi4Andrejs Matvejevs5Mario Arturo Ruiz Estrada6Information Technology Institute, Riga Technology University, LV-1048 Riga, LatviaDepartment of Internal Medicine, University of Latvia, LV-1004 Riga, LatviaTelecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, 08913 Cerdanyola del Vallès, SpainTelecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, 08913 Cerdanyola del Vallès, SpainHuman-Computer Interaction and Visual Analytics, Darmstadt University of Applied Sciences, 64295 Darmstadt, GermanyInstitute of Applied Mathematics, Riga Technology University, LV-1048 Riga, LatviaFaculty of Economics and Administration, University of Malaya, Kuala Lumpur 0603, MalaysiaAs 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.https://www.mdpi.com/2227-7390/13/1/91epidemiological modelsmathematical modelingmalware spread modelingsociotechnical systemssimulation |
| spellingShingle | Egils Ginters Uga Dumpis Laura Calvet Liñán Miquel Angel Piera Eroles Kawa Nazemi Andrejs Matvejevs Mario Arturo Ruiz Estrada A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks Mathematics epidemiological models mathematical modeling malware spread modeling sociotechnical systems simulation |
| title | A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks |
| title_full | A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks |
| title_fullStr | A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks |
| title_full_unstemmed | A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks |
| title_short | A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks |
| title_sort | paradigm for modeling infectious diseases assessing malware spread in early stage outbreaks |
| topic | epidemiological models mathematical modeling malware spread modeling sociotechnical systems simulation |
| url | https://www.mdpi.com/2227-7390/13/1/91 |
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