La inteligencia artificial en la detección de intrusiones en entornos de redes definidas por software (SDN).
| dc.contributor.advisor | Mejía Viteri, José Teodoro | |
| dc.contributor.author | Zambrano Moran, Janeth Lissette | |
| dc.date.accessioned | 2023-11-08T16:38:18Z | |
| dc.date.available | 2023-11-08T16:38:18Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://dspace.utb.edu.ec/handle/49000/15119 | |
| dc.description | Software Defined Networking (SDN) represents an innovation in network management by providing significant flexibility and efficiency in its operation. However, this flexibility and efficiency also brings with it significant security challenges. The steady increase in cyber threats has become a serious concern, especially due to the centralized architecture of SDN, which relies on logical controllers to monitor data flows throughout the network. The proposed solution includes the application of artificial intelligence (AI) in attack mitigation, leveraging the ability to analyze data and detect anomalous patterns in real time, this involves proactively identifying threats and vulnerabilities in SDN environments. This is justified by the need to address emerging threats in SDN and ensure network security and service availability. This study is based on a literature review and highlights the importance of machine learning algorithms, to improve intrusion detection in SDN environments. Implementing AI in SDN security goes beyond detecting threats and effectively protecting network resources and services. This is a key element to ensure the resilience and reliability of SDN in a constantly evolving cyber threat e environment. | es_ES |
| dc.description | Software Defined Networking (SDN) represents an innovation in network management by providing significant flexibility and efficiency in its operation. However, this flexibility and efficiency also brings with it significant security challenges. The steady increase in cyber threats has become a serious concern, especially due to the centralized architecture of SDN, which relies on logical controllers to monitor data flows throughout the network. The proposed solution includes the application of artificial intelligence (AI) in attack mitigation, leveraging the ability to analyze data and detect anomalous patterns in real time, this involves proactively identifying threats and vulnerabilities in SDN environments. This is justified by the need to address emerging threats in SDN and ensure network security and service availability. This study is based on a literature review and highlights the importance of machine learning algorithms, to improve intrusion detection in SDN environments. Implementing AI in SDN security goes beyond detecting threats and effectively protecting network resources and services. This is a key element to ensure the resilience and reliability of SDN in a constantly evolving cyber threat e environment. | es_ES |
| dc.description.abstract | Las Redes Definidas por Software (SDN) representan una innovación en la gestión de redes al proporcionar una flexibilidad y eficiencia significativas en su funcionamiento. Sin embargo, esta flexibilidad y eficiencia también conlleva importantes desafíos de seguridad. El aumento constante de las amenazas cibernéticas se ha convertido en una seria preocupación, especialmente debido a la arquitectura centralizada de SDN, que se basa en controladores lógicos para monitorear los flujos de datos en toda la red. La solución propuesta incluye la aplicación de inteligencia artificial (IA) en la mitigación de ataques, aprovechando la capacidad de analizar datos y detectar patrones anómalos en tiempo real, esto implica la identificación proactiva de amenazas y vulnerabilidades en los entornos de SDN. Esto se justifica por la necesidad de abordar las amenazas emergentes en SDN y garantizar la seguridad de la red y la disponibilidad del servicio. Este estudio se basa en una revisión de la literatura y destaca la importancia de los algoritmos de aprendizaje automático, para mejorar la detección de intrusiones en entornos SDN. La implementación de IA en la seguridad de SDN va más allá de detectar amenazas y proteger eficazmente los recursos y servicios de la red. Este es un elemento clave para garantizar la resiliencia y confiabilidad de SDN en un entorno de amenazas cibernéticas en constante evolución. | es_ES |
| dc.format.extent | 58 p. | es_ES |
| dc.language.iso | es | es_ES |
| dc.publisher | Babahoyo: UTB-FAFI. 2023 | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Ecuador | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ec/ | * |
| dc.subject | SDN | es_ES |
| dc.subject | Inteligencia Artificial | es_ES |
| dc.subject | Detección de Intrusiones | es_ES |
| dc.subject | Ciberseguridad | es_ES |
| dc.subject | Aprendizaje Automático | es_ES |
| dc.subject | Arquitectura SDN | es_ES |
| dc.subject | Aprendizaje profundo | es_ES |
| dc.subject | Controlador SDN | es_ES |
| dc.subject | Vulnerabilidades de SDN | es_ES |
| dc.subject | OpenFlow | es_ES |
| dc.subject | Centralización de Control | es_ES |
| dc.title | La inteligencia artificial en la detección de intrusiones en entornos de redes definidas por software (SDN). | es_ES |
| dc.type | bachelorThesis | es_ES |
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