The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation
With IoT networks' rapid advancement and significant cybersecurity challenges, the proposal and analysis of models capable of studying malware propagation within these structures have become highly relevant. This paper aims to formulate and implement a SEIRS-NIMFA model to analyze the propagati...
- Autores:
-
Quiroga Sánchez, Laura Gabriela
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/68320
- Acceso en línea:
- http://hdl.handle.net/1992/68320
- Palabra clave:
- IoT networks
Epidemiology
Malware propagation modeling
SEIRS
Mean-field approximation
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| title |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| spellingShingle |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation IoT networks Epidemiology Malware propagation modeling SEIRS Mean-field approximation Ingeniería |
| title_short |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| title_full |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| title_fullStr |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| title_full_unstemmed |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| title_sort |
The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation |
| dc.creator.fl_str_mv |
Quiroga Sánchez, Laura Gabriela |
| dc.contributor.advisor.none.fl_str_mv |
Montoya Orozco, Germán Adolfo Lozano Garzon, Carlos Andres |
| dc.contributor.author.none.fl_str_mv |
Quiroga Sánchez, Laura Gabriela |
| dc.subject.keyword.none.fl_str_mv |
IoT networks Epidemiology Malware propagation modeling SEIRS Mean-field approximation |
| topic |
IoT networks Epidemiology Malware propagation modeling SEIRS Mean-field approximation Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
With IoT networks' rapid advancement and significant cybersecurity challenges, the proposal and analysis of models capable of studying malware propagation within these structures have become highly relevant. This paper aims to formulate and implement a SEIRS-NIMFA model to analyze the propagation of malware infections with a latency period. To achieve this, a SEIRS model was mathematically described using an individual-based approach and then implemented using Python. This study examines the effects of varying network topology, the initially infected device, and the model parameters on the propagation dynamics. The results demonstrate that this novel model can effectively support the decision-making processes for implementing security measures in real-life scenarios. Furthermore, the model and its implementation are open to further extensions, enhancing their potential applicability. |
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2023 |
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2023-07-11T18:21:48Z |
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2023-07-11T18:21:48Z |
| dc.date.issued.none.fl_str_mv |
2023-07-07 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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http://hdl.handle.net/1992/68320 |
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eng |
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eng |
| dc.relation.references.es_CO.fl_str_mv |
SonicWall. Mid-year update: 2022 SonicWall Cyber Threat Report. https://www.sonicwall.com/medialibrary/en/white-paper/mid-year-2022-cyber-threat-report.pdf. (2022-MidYearThreatReport-JK-6641). 2022. A. Abusitta et al. 'Deep learning-enabled anomaly detection for IoT systems'. In: Internet of Things 21(2023). DOI: 10.1016/j.iot.2022.100656. M. Antonakakis et al. 'Understanding the Mirai Botnet'. In: 26th USENIX Security Symposium (USENIX Security 17). USENIX Association. Vancouver, BC, 2017, pp. 1093-1110. P. Van Mieghem. 'The N-intertwined SIS epidemic network model'. In: Computing 93 (2011), pp. 147-169. DOI: 10.1007/s00607-011-0155-y I. Martínez et al. 'MalSEIRS: Forecasting Malware Spread Based on Compartmental Models in Epidemiology'. In: Complexity 2021 (2021), Article ID 5415724. DOI: 10.1155/2021/5415724. A. Mahboubi, S. Camtepe, and K. Ansari. 'Stochastic modeling of IoT botnet spread: A short survey on mobile malware spread modeling'. In: IEEE Access (2020). DOI: 10.1109/ACCESS.2020.3044277. S. Bonaccorsi and S. Turri. 'Deterministic and Stochastic Mean-Field SIRS Models on Heterogeneous Networks'. In: Discrete and Continuous Models in the Theory of Networks. Ed. by Fatihcan M. Atay, Pavel B. Kurasov, and Delio Mugnolo. Vol. 281. Birkhauser Cham, 2020, pp. 67-89. DOI: 10.1007/978-3-030-44097-8 K. L. Lueth et al. Market Insights for the Internet of Things: State of IoT - Spring 2022. IoT Analytics. 2022. URL: https://iot-analytics.com/product/state-of-iot-spring-2022/. P. Kumari and A. K. Jain. 'A comprehensive study of DDoS attacks over IoT network and their countermeasures'. In: Computers and Security 127 (2023). DOI: 10.1016/j.cose.2023.103096. A. A. Haghrah, S. Ghaemi, and M. A. Badamchizadeh. 'Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention'. In: Artificial Intelligence in Medicine 134 (2022). DOI: 10.1016/j.artmed.2022.102422. N. Groves-Kirkby et al. 'Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning'. In: Epidemics 42 (2023). DOI: 10.1016/j.epidem.2022.100662. M. Galván. 'NIMFA Epidemiological model for studying malware behavior in IoT Networks'. Undergraduate thesis. Bogota, Colombia, 2021. J. Flórez. 'Modelo epidemiológico SIS para estudiar el comportamiento del malware en redes IoT'. Undergraduate thesis. Bogota, Colombia, 2021. O. N. Bjørnstad et al. 'The SEIRS model for infectious disease dynamics'. In: Nature Methods 17.6 (2020), pp. 557-558. DOI: 10.1038/s41592-020-0856-2. |
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Facultad de Ingeniería |
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Departamento de Ingeniería Sistemas y Computación |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Montoya Orozco, Germán Adolfoc25c116e-3304-4817-b93f-a89766e4dbfc600Lozano Garzon, Carlos Andresvirtual::5959-1Quiroga Sánchez, Laura Gabriela277ad675-228c-4a36-936d-81ad9a6c9a6b6002023-07-11T18:21:48Z2023-07-11T18:21:48Z2023-07-07http://hdl.handle.net/1992/68320instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/With IoT networks' rapid advancement and significant cybersecurity challenges, the proposal and analysis of models capable of studying malware propagation within these structures have become highly relevant. This paper aims to formulate and implement a SEIRS-NIMFA model to analyze the propagation of malware infections with a latency period. To achieve this, a SEIRS model was mathematically described using an individual-based approach and then implemented using Python. This study examines the effects of varying network topology, the initially infected device, and the model parameters on the propagation dynamics. The results demonstrate that this novel model can effectively support the decision-making processes for implementing security measures in real-life scenarios. Furthermore, the model and its implementation are open to further extensions, enhancing their potential applicability.Ingeniero de Sistemas y ComputaciónPregrado16 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónThe SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagationTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPIoT networksEpidemiologyMalware propagation modelingSEIRSMean-field approximationIngenieríaSonicWall. Mid-year update: 2022 SonicWall Cyber Threat Report. https://www.sonicwall.com/medialibrary/en/white-paper/mid-year-2022-cyber-threat-report.pdf. (2022-MidYearThreatReport-JK-6641). 2022.A. Abusitta et al. 'Deep learning-enabled anomaly detection for IoT systems'. In: Internet of Things 21(2023). DOI: 10.1016/j.iot.2022.100656.M. Antonakakis et al. 'Understanding the Mirai Botnet'. In: 26th USENIX Security Symposium (USENIX Security 17). USENIX Association. Vancouver, BC, 2017, pp. 1093-1110.P. Van Mieghem. 'The N-intertwined SIS epidemic network model'. In: Computing 93 (2011), pp. 147-169. DOI: 10.1007/s00607-011-0155-yI. Martínez et al. 'MalSEIRS: Forecasting Malware Spread Based on Compartmental Models in Epidemiology'. In: Complexity 2021 (2021), Article ID 5415724. DOI: 10.1155/2021/5415724.A. Mahboubi, S. Camtepe, and K. Ansari. 'Stochastic modeling of IoT botnet spread: A short survey on mobile malware spread modeling'. In: IEEE Access (2020). DOI: 10.1109/ACCESS.2020.3044277.S. Bonaccorsi and S. Turri. 'Deterministic and Stochastic Mean-Field SIRS Models on Heterogeneous Networks'. In: Discrete and Continuous Models in the Theory of Networks. Ed. by Fatihcan M. Atay, Pavel B. Kurasov, and Delio Mugnolo. Vol. 281. Birkhauser Cham, 2020, pp. 67-89. DOI: 10.1007/978-3-030-44097-8K. L. Lueth et al. Market Insights for the Internet of Things: State of IoT - Spring 2022. IoT Analytics. 2022. URL: https://iot-analytics.com/product/state-of-iot-spring-2022/.P. Kumari and A. K. Jain. 'A comprehensive study of DDoS attacks over IoT network and their countermeasures'. In: Computers and Security 127 (2023). DOI: 10.1016/j.cose.2023.103096.A. A. Haghrah, S. Ghaemi, and M. A. Badamchizadeh. 'Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention'. In: Artificial Intelligence in Medicine 134 (2022). DOI: 10.1016/j.artmed.2022.102422.N. Groves-Kirkby et al. 'Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning'. In: Epidemics 42 (2023). DOI: 10.1016/j.epidem.2022.100662.M. Galván. 'NIMFA Epidemiological model for studying malware behavior in IoT Networks'. Undergraduate thesis. Bogota, Colombia, 2021.J. Flórez. 'Modelo epidemiológico SIS para estudiar el comportamiento del malware en redes IoT'. Undergraduate thesis. Bogota, Colombia, 2021.O. N. Bjørnstad et al. 'The SEIRS model for infectious disease dynamics'. In: Nature Methods 17.6 (2020), pp. 557-558. 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