dc.contributor | Lozano Garzón, Carlos Andrés | |
dc.contributor | Montoya Orozco, Germán Adolfo | |
dc.contributor | COMIT | |
dc.creator | González Gómez, Juan Diego | |
dc.date.accessioned | 2022-07-18T18:23:48Z | |
dc.date.available | 2022-07-18T18:23:48Z | |
dc.date.created | 2022-07-18T18:23:48Z | |
dc.date.issued | 2022-07-15 | |
dc.identifier | http://hdl.handle.net/1992/58928 | |
dc.identifier | instname:Universidad de los Andes | |
dc.identifier | reponame:Repositorio Institucional Séneca | |
dc.identifier | repourl:https://repositorio.uniandes.edu.co/ | |
dc.description.abstract | En el mundo de la computación existen múltiples amenazas para los sistemas
informáticos. Una de estas son las botnets, las cuales pueden ser bastante peligrosas
según el tipo de malware que propaguen. Por este motivo, es de gran importancia
estudiarlas, así como su comportamiento y evolución. Este proyecto de grado tiene este
propósito, estudiar el comportamiento de las botnets a través del diseño e implementación
de un modelo epidemiológico SIRS, integrado con un modelo NIMFA. Para lograr esto, se
desarrolló una aplicación en Python que permitiera ejecutar el modelo planteado variando
sus parámetros, y así poder validar los resultados que se obtuvieran de este. | |
dc.description.abstract | In the world of computing there are multiple threats to computer systems. One of these are
botnets, which can be quite dangerous depending on the type of malware they spread. For
this reason, it is extremely important to study them, as well as their behavior and evolution.
This degree project has this purpose, to study the behavior of botnets through the design
and implementation of a SIRS epidemiological model, integrated with a NIMFA model. To
achieve this, a Python application was developed that would allow the proposed model to
be executed varying its parameters, and thus be able to validate the results obtained from
it. | |
dc.language | spa | |
dc.publisher | Universidad de los Andes | |
dc.publisher | Ingeniería de Sistemas y Computación | |
dc.publisher | Facultad de Ingeniería | |
dc.publisher | Departamento de Ingeniería Sistemas y Computación | |
dc.relation | Kaspersky (s.f.). What is a Botnet? Tomado de: https://usa.kaspersky.com/resourcecenter/threats/botnet-attacks | |
dc.relation | Cimpanu, C. (2022). FritzFrog botnet returns with new attacks after more than a year of
inactivity. Tomado de: https://therecord.media/fritzfrog-botnet-returns-with-new-attacksafter-more-than-a-year-of-inactivity/ | |
dc.relation | Sharma, A. (2020). FritzFrog malware attacks Linux servers over SSH to mine Monero.
Tomado de: https://www.bleepingcomputer.com/news/security/fritzfrog-malware-attackslinux-servers-over-ssh-to-mine-monero/ | |
dc.relation | Florez, J. (2021). Modelo epidemiológico SIS para estudiar el comportamiento del
malware en redes IoT. Tomado de:
https://repositorio.uniandes.edu.co/bitstream/handle/1992/53708/24714.pdf | |
dc.relation | Galvan, M. (2021). NIMFA Epidemiological model for studying malware behavior in IoT
Networks. Tomado de:
https://repositorio.uniandes.edu.co/bitstream/handle/1992/55214/26425.pdf | |
dc.relation | Mahboubi, A., Camtepe, S. y Ansari, K. (2016). Stochastic modeling of IoT Botnet
spread: A short survey on mobile malware spread modeling. Tomado de:
https://www.researchgate.net/publication/347631084 | |
dc.relation | Banks, S. y Stytz, M. (2011). Advancing botnet modeling techniques for military and
security simulations. Tomado de: https://www.spiedigitallibrary.org/conference-
42 Modelo epidemiológico SIRS para estudiar el comportamiento de botnets
proceedings-of-spie/8060/80600I/Advancing-botnet-modeling-techniques-for-military-andsecurity-simulations/10.1117/12.882892.short | |
dc.relation | Bautista, Z. (2021). Modelado PSCI de botnets en redes IoT con Cadenas de Márkov
Múltiples. Tomado de:
https://repositorio.uniandes.edu.co/bitstream/handle/1992/55655/26456.pdf | |
dc.relation | Van Mieghem, P. (2011). The N-intertwined SIS epidemic network model. Tomado de:
https://nas.ewi.tudelft.nl/people/Piet/papers/Computing2011_N_intertwined_SIS_virus_sp
read.pdf | |
dc.relation | Prasse, B. y Van Mieghem, P. (2019). The Viral State Dynamics of the Discrete-Time
NIMFA Epidemic Model. Tomado de: https://arxiv.org/abs/1903.08027 | |
dc.relation | Mahboubi, A., Camtepe, S. y Ansari, K. (2020) Stochastic Modeling of IoT Botnet
Spread: A Short Survey on Mobile Malware Spread Modeling. Tomado de:
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9293016 | |
dc.relation | Liu, W. y van den Driessche, P. (1995). Epidemiological models with varying
population size and dose-dependent latent period. Tomado de:
https://www.sciencedirect.com/science/article/abs/pii/002555649400067A | |
dc.relation | El-Saka, H. (2014). The fractional-order SIS epidemic model with variable population
size. Tomado de: https://www.researchgate.net/publication/260427881 | |
dc.relation | Di Troia, F., Aaron, C. y Austin, T. (2017). SocioBot: A Twitter-based botnet. Tomado
de: https://www.researchgate.net/publication/312126333 | |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | |
dc.rights | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | http://purl.org/coar/access_right/c_abf2 | |
dc.title | Modelo epidemiológico SIRS para estudiar el comportamiento de botnets | |
dc.type | Trabajo de grado - Pregrado | |