Ítem
Acceso Abierto
Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning
| dc.contributor.advisor | Díaz López, Daniel Orlando | |
| dc.creator | Tristancho Muñoz, Miguel Angel | |
| dc.creator.degree | Magíster en Matemáticas Aplicadas y Ciencias de la Computación | |
| dc.creator.degreetype | Full time | |
| dc.date.accessioned | 2023-03-24T21:27:37Z | |
| dc.date.available | 2023-03-24T21:27:37Z | |
| dc.date.created | 2023-02-07 | |
| dc.description | Establecer un sistema de análisis de tráfico de red basado en algoritmos de machine learning (ML), orientado a sistemas de control industrial que permita: la identificación de comportamientos anormales para evitar la explotación de vulnerabilidades que afecten la seguridad de procesos industriales reduciendo riesgos de disponibilidad y soporte la continuidad del negocio. | |
| dc.description.abstract | The growing development of computer networks associated with industrial systems and their integration with corporate networks (Internet) have made this group a desired target for cybercriminals worldwide. Mitigating this type of risk is one of the highest priorities for integrators, manufacturers, and users of control systems due to the great impact that can occur on the economy, the environment and the people in an organization when materialization occurs. of an attempted attack or sabotage of industrial processes. It is becoming increasingly important for industrial organizations to become aware of the weakness of these systems and seek organizational structures for security management that help them optimize their protection against external threats from all points of view to detect and address incidents. security-related issues before they become a major problem. | |
| dc.format.extent | 78 pp | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.48713/10336_38272 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/38272 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad del Rosario | spa |
| dc.publisher.department | Escuela de Ingeniería, Ciencia y Tecnología | spa |
| dc.publisher.program | Maestría en Matemáticas Aplicadas y Ciencias de la Computación | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.accesRights | info:eu-repo/semantics/openAccess | |
| dc.rights.acceso | Abierto (Texto Completo) | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
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| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | spa |
| dc.subject | Machine learning | |
| dc.subject | Sistemas de control industrial ICS | |
| dc.subject | Tráfico de red industrial | |
| dc.subject | Detección de anomalías | |
| dc.subject | Reducción de riesgos en seguridad de procesos industriales | |
| dc.subject.keyword | Machine Learning | |
| dc.subject.keyword | Cibersecurity | |
| dc.title | Detección de anomalías en tráfico de red de Sistemas de Control Industrial soportada en algoritmos de machine learning | |
| dc.title.TranslatedTitle | Detection of anomalies in red traffic of Industrial Control Systems supported by machine learning algorithms | |
| dc.type | bachelorThesis | |
| dc.type.document | Trabajo de grado | |
| dc.type.spa | Trabajo de grado |
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