The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks
Abstract With the rapid advancement of Internet of Things networks and its significant cybersecurity challenges, the proposal of models capable of studying malware propagation within these structures has become highly relevant. This paper aims to formulate and implement an SEIRS-NIMFA model to analy...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Cybersecurity |
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| Online Access: | https://doi.org/10.1186/s42400-024-00310-z |
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| author | Laura Quiroga-Sánchez Germán A. Montoya Carlos Lozano-Garzon |
| author_facet | Laura Quiroga-Sánchez Germán A. Montoya Carlos Lozano-Garzon |
| author_sort | Laura Quiroga-Sánchez |
| collection | DOAJ |
| description | Abstract With the rapid advancement of Internet of Things networks and its significant cybersecurity challenges, the proposal of models capable of studying malware propagation within these structures has become highly relevant. This paper aims to formulate and implement an SEIRS-NIMFA model to analyze the dissemination of malware infections with a latency period. To accomplish this, we mathematically articulated an SEIRS epidemiological model using an individual-based approach and implemented it using Python. In addition, this paper examines how varying the network size and density, the initially infected device, and several model parameters influence the propagation dynamics. Moreover, to address the Markov chain approach’s high temporal and spatial complexity, we use the n-intertwined mean-field approximation method. Our findings demonstrate that our proposal can effectively aid decision-making in implementing security measures in real-world situations. Finally, our proposal and its implementation are open to further enhancements, broadening their potential applications. |
| format | Article |
| id | doaj-art-b35fba3126e24560894a0a64694df8c1 |
| institution | Kabale University |
| issn | 2523-3246 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj-art-b35fba3126e24560894a0a64694df8c12025-01-12T12:28:38ZengSpringerOpenCybersecurity2523-32462025-01-018113710.1186/s42400-024-00310-zThe SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networksLaura Quiroga-Sánchez0Germán A. Montoya1Carlos Lozano-Garzon2Systems and Computing Engineering Department, Universidad de los AndesSystems and Computing Engineering Department, Universidad de los AndesSystems and Computing Engineering Department, Universidad de los AndesAbstract With the rapid advancement of Internet of Things networks and its significant cybersecurity challenges, the proposal of models capable of studying malware propagation within these structures has become highly relevant. This paper aims to formulate and implement an SEIRS-NIMFA model to analyze the dissemination of malware infections with a latency period. To accomplish this, we mathematically articulated an SEIRS epidemiological model using an individual-based approach and implemented it using Python. In addition, this paper examines how varying the network size and density, the initially infected device, and several model parameters influence the propagation dynamics. Moreover, to address the Markov chain approach’s high temporal and spatial complexity, we use the n-intertwined mean-field approximation method. Our findings demonstrate that our proposal can effectively aid decision-making in implementing security measures in real-world situations. Finally, our proposal and its implementation are open to further enhancements, broadening their potential applications.https://doi.org/10.1186/s42400-024-00310-zIoT networksEpidemiologyMalware propagation modelingSEIRSMean-field approximation |
| spellingShingle | Laura Quiroga-Sánchez Germán A. Montoya Carlos Lozano-Garzon The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks Cybersecurity IoT networks Epidemiology Malware propagation modeling SEIRS Mean-field approximation |
| title | The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks |
| title_full | The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks |
| title_fullStr | The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks |
| title_full_unstemmed | The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks |
| title_short | The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks |
| title_sort | seirs nimfa epidemiological model for malware propagation analysis in iot networks |
| topic | IoT networks Epidemiology Malware propagation modeling SEIRS Mean-field approximation |
| url | https://doi.org/10.1186/s42400-024-00310-z |
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