| dc.contributor | Lozano Garzón, Carlos Andrés | |
| dc.contributor | Montoya Orozco, Germán Adolfo | |
| dc.contributor | COMIT | |
| dc.creator | Jaimes Bastidas, Elina Valentina | |
| dc.date.accessioned | 2022-07-19T13:18:12Z | |
| dc.date.available | 2022-07-19T13:18:12Z | |
| dc.date.created | 2022-07-19T13:18:12Z | |
| dc.date.issued | 2022-07-14 | |
| dc.identifier | http://hdl.handle.net/1992/58981 | |
| dc.identifier | instname:Universidad de los Andes | |
| dc.identifier | reponame:Repositorio Institucional Séneca | |
| dc.identifier | repourl:https://repositorio.uniandes.edu.co/ | |
| dc.description.abstract | El Internet de las Cosas (IoT) es uno de los campos tecnológicos emergentes con mayor
evolución en los últimos años. Dado este incremento exponencial, se han plantado nuevos
retos de seguridad en cuanto a las redes IoT, puntualmente sobre los ataques que puedan
propagarse en la misma. En este trabajo se implementa un modelo híbrido que combina
técnicas de Machine Learning y Deep Learning con el propósito de detectar ataques de
tipo DoS de manera temprana. Las técnicas que componen el modelo son una Red
Neuronal Convolucional y Random Forest. El modelo híbrido presentó un desempeño
favorable, clasificando satisfactoriamente más del 99% de los ataques y refinando la
clasificación de trazas etiquetadas erróneamente en un 41%. | |
| 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 | |
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| dc.rights | Atribución-CompartirIgual 4.0 Internacional | |
| dc.rights | Atribución-CompartirIgual 4.0 Internacional | |
| dc.rights | http://creativecommons.org/licenses/by-sa/4.0/ | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.title | Detección de amenazas en redes IoT empleando modelo híbrido de Machine Learning y Deep Learning | |
| dc.type | Trabajo de grado - Pregrado | |
