Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks
Una botnet es un conjunto de dispositivos que están bajo el control de un atacante y se utilizan para llevar a cabo actividades maliciosas contra una víctima. En este contexto, comprender el comportamiento de este tipo de malware es crucial para proteger las redes. Por ello, se propone un enfoque de...
- Autores:
-
Pinto Morato, Lindsay Vanessa
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74522
- Acceso en línea:
- https://hdl.handle.net/1992/74522
- Palabra clave:
- Botnet
SEIRS-NIMFA
Modelo epidemiológico
Topologías
Epidemiological modeling
Topologies
Ingeniería
- Rights
- embargoedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| title |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| spellingShingle |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks Botnet SEIRS-NIMFA Modelo epidemiológico Topologías Epidemiological modeling Topologies Ingeniería |
| title_short |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| title_full |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| title_fullStr |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| title_full_unstemmed |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| title_sort |
Epidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT Networks |
| dc.creator.fl_str_mv |
Pinto Morato, Lindsay Vanessa |
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Lozano Garzón, Carlos Andrés Montoya Orozco, Germán Adolfo |
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Pinto Morato, Lindsay Vanessa |
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Facultad de Ingeniería::COMIT - Comunicaciones y Tecnología de Información |
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Botnet SEIRS-NIMFA |
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Botnet SEIRS-NIMFA Modelo epidemiológico Topologías Epidemiological modeling Topologies Ingeniería |
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Modelo epidemiológico Topologías |
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Epidemiological modeling Topologies |
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Ingeniería |
| description |
Una botnet es un conjunto de dispositivos que están bajo el control de un atacante y se utilizan para llevar a cabo actividades maliciosas contra una víctima. En este contexto, comprender el comportamiento de este tipo de malware es crucial para proteger las redes. Por ello, se propone un enfoque de modelado epidemiológico para explicar el comportamiento de las botnets, centrándose especialmente en el Botnet MIRAI. El modelo seleccionado, SEIRS-NIMFA, fue adaptado para incluir tasas relacionadas con el ciclo de vida de la botnet, como tasas de actualización, escaneo e inyección. Estas modificaciones se basaron en datos recopilados del botnet MIRAI, y también se probaron variaciones utilizando tasas del modelo SEIRS ajustadas específicamente para esta botnet. La implementación del modelo se llevó a cabo mediante un programa en Python que utiliza el método de Runge-Kutta para resolver el sistema de ecuaciones diferenciales. El análisis proporcionó conocimientos significativos sobre los nodos críticos y el comportamiento de la red en diferentes topologías. En las topologías de estrella y bus, los nodos centrales surgieron como los más críticos, mientras que en las topologías de malla, el comportamiento de la red resultó ser dependiente de la densidad del grafo en lugar del número de nodos. Estos hallazgos ofrecen una valiosa orientación para los administradores de redes, ayudándoles a identificar topologías críticas y nodos vulnerables susceptibles a tales ataques. |
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M. A. Masriah, "Battling Botnets with AI: A New Era in Cybersecurity," September 2023. [Online]. Available: https://www.linkedin.com/pulse/battling-botnets-ai-new-era-cybersecurity-majd-aldeen-masriah/. M. D. Galvan, "NIMFA Epidemiological model for studying malware behavior in IoT Networks," Bogota, Colombia, 2021. S. Delgado, "Una nueva botnet deja en jaque a miles de usuarios de WordPress," Bitlifemedia, March 2024. [Online]. Available: https://bitlifemedia.com/2024/03/nueva-botnet-usuarios-wordpress/. I. Belcic, "Qué es una botnet," Avast, October 2021. [Online]. Available: https://www.avast.com/es-es/c-botnet#:~:text=Las%20tres%20etapas%20de%20la,%3A%20infecci%C3%B3n%2C%20expansi%C3%B3n%20y%20ataque.. F. Hussain et al., "A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks," IEEE Access, vol. 9, no. doi: 10.1109/ACCESS.2021.3131014, pp. 163412-163430, 2021. G. Barakat, B. Al-Duwairi, M. Jarrah and M. Jaradat, "Modeling and Simulation of IoT Botnet Behaviors Using DEVS," 2022 13th International Conference on Information and Communication Systems (ICICS), no. doi: 10.1109/ICICS55353.2022.9811164, pp. 42-47, 2022. L. Gordis, "Chapter 1," in Epidemiology, Fifth Edition, Canada, Elsevier, 2014, pp. 2-3. Wikipedia, "Compartmental models in epidemiology," Wikipedia, s.f. [Online]. Available: https://en.m.wikipedia.org/wiki/Compartmental_models_in_epidemiology#. J. F. Balarezo, S. Wang, K. Gomez Chavez, A. Al-Hourani and S. Kandeepan,, "Dynamics of Botnet Propagation in Software Defined Networks Using Epidemic Models," IEEE Access, vol. 9, no. doi: 10.1109/ACCESS.2021.3108181, pp. 119406-119417, 2021. I. Martínez Martínez, A. Florián Quitián, D. Díaz-López, P. Nespoli y F. Gómez Mármol, "Modeling and Propagation Dynamics Analysis of Complex Networks," Complexity, vol. 2021, no. doi.org/10.1155/2021/5415724, pp. 1-19, 2021. D. Keliger and I. Horváth, "Accuracy criterion for mean field approximations of Markov processes on hypergraphs," Physica A: Statistical Mechanics and its Applications, vol. 609, no. https://doi.org/10.1016/j.physa.2022.128370., pp. 1-3, 2023. P. Van Mieghem, "The N-intertwined SIS epidemic network model," Computing, vol. 93, no. https://doi.org/10.1007/s00607-011-0155-y, 2011. L. Quiroga, "SEIRS-NIMFA Epidemiological Model for Analyzing Malware Propagation in IoT Networks," Bogotá, Colombia, 2023. M. A. Fatihcan , K. Pavel B. and D. Mugnolo, Discrete and Continuous Models in the Theory of Networks, Switzerland: Birkhäuser,, 2020. Cloudflare, "Cloudflare," Inside the infamous Mirai IoT Botnet: A Retrospective Analysis, December 2017. [Online]. Available: https://blog.cloudflare.com/inside-mirai-the-infamous-iot-botnet-a-retrospective-analysis. European Union Agency for Cybersecurity, "Botnet ENISA Threat Landscape," Eninsa, 2020. M. Estan, "TOPOLOGÍAS," Universidad de Huelva, May 2012. [Online]. Available: https://uhu.es/antonio.barragan/content/5topologias. L. Quiroga, "Github," SEIRSModel, [Online]. Available: https://github.com/Lauraquiroga/SEIRSModel. Manos, A et al. , "Understanding the Mirai Botnet," Proceedings of the 26th USENIX Security Symposium, vol. 26, 2017. PingIdentity, "What Is a Botnet Attack and How to Prevent It.," PingIdentity, [Online]. Available: https://www.pingidentity.com/en/resources/cybersecurity-fundamentals/threats/botnet-attack.html. |
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Lozano Garzón, Carlos Andrésvirtual::18784-1Montoya Orozco, Germán Adolfovirtual::18785-1Pinto Morato, Lindsay VanessaFacultad de Ingeniería::COMIT - Comunicaciones y Tecnología de Información2024-07-12T21:01:10Z2025-07-292024-07-122024-07-12https://hdl.handle.net/1992/74522instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Una botnet es un conjunto de dispositivos que están bajo el control de un atacante y se utilizan para llevar a cabo actividades maliciosas contra una víctima. En este contexto, comprender el comportamiento de este tipo de malware es crucial para proteger las redes. Por ello, se propone un enfoque de modelado epidemiológico para explicar el comportamiento de las botnets, centrándose especialmente en el Botnet MIRAI. El modelo seleccionado, SEIRS-NIMFA, fue adaptado para incluir tasas relacionadas con el ciclo de vida de la botnet, como tasas de actualización, escaneo e inyección. Estas modificaciones se basaron en datos recopilados del botnet MIRAI, y también se probaron variaciones utilizando tasas del modelo SEIRS ajustadas específicamente para esta botnet. La implementación del modelo se llevó a cabo mediante un programa en Python que utiliza el método de Runge-Kutta para resolver el sistema de ecuaciones diferenciales. El análisis proporcionó conocimientos significativos sobre los nodos críticos y el comportamiento de la red en diferentes topologías. En las topologías de estrella y bus, los nodos centrales surgieron como los más críticos, mientras que en las topologías de malla, el comportamiento de la red resultó ser dependiente de la densidad del grafo en lugar del número de nodos. Estos hallazgos ofrecen una valiosa orientación para los administradores de redes, ayudándoles a identificar topologías críticas y nodos vulnerables susceptibles a tales ataques.A botnet is a set of devices under an attacker's control and used to carry out malicious activities against a victim. In this context, understanding the behavior of this type of malware is crucial to protect networks. Therefore, an epidemiological modeling approach is proposed to explain the behavior of botnets, focusing especially on the MIRAI Botnet. The selected model, SEIRS-NIMFA, was adapted to include rates related to the botnet's lifecycle, such as update rates, scanning, and injection rates. These modifications were based on data collected from the MIRAI botnet, and variations were also tested using rates from the SEIRS model specifically adjusted for this botnet. The model implementation was carried out through a Python program that uses the Runge-Kutta method to solve the system of differential equations. The analysis provided insights into critical nodes and network behavior in different topologies. In star and bus topologies, central nodes emerged as the most critical. In contrast, in mesh topologies, network behavior was found to be dependent on graph density rather than the number of nodes. These findings offer valuable guidance for network administrators, helping them identify critical topologies and vulnerable nodes susceptible to such attacks.Pregrado75 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería de Sistemas y ComputaciónAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfEpidemiological SEIRS-NIMFA Model for Analyzing Botnet Spread in IoT NetworksTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPBotnetSEIRS-NIMFAModelo epidemiológicoTopologíasEpidemiological modelingTopologiesIngenieríaM. A. Masriah, "Battling Botnets with AI: A New Era in Cybersecurity," September 2023. [Online]. Available: https://www.linkedin.com/pulse/battling-botnets-ai-new-era-cybersecurity-majd-aldeen-masriah/.M. D. Galvan, "NIMFA Epidemiological model for studying malware behavior in IoT Networks," Bogota, Colombia, 2021.S. Delgado, "Una nueva botnet deja en jaque a miles de usuarios de WordPress," Bitlifemedia, March 2024. [Online]. Available: https://bitlifemedia.com/2024/03/nueva-botnet-usuarios-wordpress/.I. Belcic, "Qué es una botnet," Avast, October 2021. [Online]. Available: https://www.avast.com/es-es/c-botnet#:~:text=Las%20tres%20etapas%20de%20la,%3A%20infecci%C3%B3n%2C%20expansi%C3%B3n%20y%20ataque..F. Hussain et al., "A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks," IEEE Access, vol. 9, no. doi: 10.1109/ACCESS.2021.3131014, pp. 163412-163430, 2021.G. Barakat, B. Al-Duwairi, M. Jarrah and M. Jaradat, "Modeling and Simulation of IoT Botnet Behaviors Using DEVS," 2022 13th International Conference on Information and Communication Systems (ICICS), no. doi: 10.1109/ICICS55353.2022.9811164, pp. 42-47, 2022.L. Gordis, "Chapter 1," in Epidemiology, Fifth Edition, Canada, Elsevier, 2014, pp. 2-3.Wikipedia, "Compartmental models in epidemiology," Wikipedia, s.f. [Online]. Available: https://en.m.wikipedia.org/wiki/Compartmental_models_in_epidemiology#.J. F. Balarezo, S. Wang, K. Gomez Chavez, A. Al-Hourani and S. Kandeepan,, "Dynamics of Botnet Propagation in Software Defined Networks Using Epidemic Models," IEEE Access, vol. 9, no. doi: 10.1109/ACCESS.2021.3108181, pp. 119406-119417, 2021.I. Martínez Martínez, A. Florián Quitián, D. Díaz-López, P. Nespoli y F. Gómez Mármol, "Modeling and Propagation Dynamics Analysis of Complex Networks," Complexity, vol. 2021, no. doi.org/10.1155/2021/5415724, pp. 1-19, 2021.D. Keliger and I. Horváth, "Accuracy criterion for mean field approximations of Markov processes on hypergraphs," Physica A: Statistical Mechanics and its Applications, vol. 609, no. https://doi.org/10.1016/j.physa.2022.128370., pp. 1-3, 2023.P. Van Mieghem, "The N-intertwined SIS epidemic network model," Computing, vol. 93, no. https://doi.org/10.1007/s00607-011-0155-y, 2011.L. Quiroga, "SEIRS-NIMFA Epidemiological Model for Analyzing Malware Propagation in IoT Networks," Bogotá, Colombia, 2023.M. A. Fatihcan , K. Pavel B. and D. Mugnolo, Discrete and Continuous Models in the Theory of Networks, Switzerland: Birkhäuser,, 2020.Cloudflare, "Cloudflare," Inside the infamous Mirai IoT Botnet: A Retrospective Analysis, December 2017. [Online]. Available: https://blog.cloudflare.com/inside-mirai-the-infamous-iot-botnet-a-retrospective-analysis.European Union Agency for Cybersecurity, "Botnet ENISA Threat Landscape," Eninsa, 2020.M. Estan, "TOPOLOGÍAS," Universidad de Huelva, May 2012. [Online]. Available: https://uhu.es/antonio.barragan/content/5topologias.L. Quiroga, "Github," SEIRSModel, [Online]. Available: https://github.com/Lauraquiroga/SEIRSModel.Manos, A et al. , "Understanding the Mirai Botnet," Proceedings of the 26th USENIX Security Symposium, vol. 26, 2017.PingIdentity, "What Is a Botnet Attack and How to Prevent It.," PingIdentity, [Online]. 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