La inteligencia artificial en la detección de intrusos en infraestructura de TI.
Digitalization has turned IT infrastructures into targets of increasingly sophisticated cyber attacks, highlighting the limitations of traditional intrusion detection methods. In this context, Artificial Intelligence (AI) emerges as a key tool to improve threat identification, reduce false positives...
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| Formato: | bachelorThesis |
| Publicado: |
2025
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| Acceso en liña: | http://dspace.utb.edu.ec/handle/49000/17909 |
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Engadir etiqueta | _version_ | 1858410473043525632 |
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| author | Herrera Flores, Anthony Aron |
| author_facet | Herrera Flores, Anthony Aron |
| author_role | author |
| collection | Repositorio Universidad Técnica de Babahoyo |
| dc.contributor.none.fl_str_mv | Fernández Torres, Ana Del Rocío |
| dc.creator.none.fl_str_mv | Herrera Flores, Anthony Aron |
| dc.date.none.fl_str_mv | 2025-04-25T16:28:31Z 2025-04-25T16:28:31Z 2025 |
| dc.format.none.fl_str_mv | 39 p. application/pdf |
| dc.identifier.none.fl_str_mv | http://dspace.utb.edu.ec/handle/49000/17909 |
| dc.language.none.fl_str_mv | es |
| dc.publisher.none.fl_str_mv | Babahoyo: UTB-FAFI. 2025 |
| dc.rights.none.fl_str_mv | Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:Repositorio Universidad Técnica de Babahoyo instname:Universidad Técnica de Babahoyo instacron:UTB |
| dc.subject.none.fl_str_mv | Ciberseguridad Inteligencia Artificial Detección de intrusos Machine Learning Infraestructuras de TI Algoritmos de clasificación Redes Neuronales Sistemas de Información |
| dc.title.none.fl_str_mv | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/bachelorThesis |
| description | Digitalization has turned IT infrastructures into targets of increasingly sophisticated cyber attacks, highlighting the limitations of traditional intrusion detection methods. In this context, Artificial Intelligence (AI) emerges as a key tool to improve threat identification, reduce false positives and automate incident response. This study analyzes the impact of AI in cybersecurity, comparing algorithms such as Random Forest, Neural Networks and Support Vector Machines (SVM) in intrusion detection. In addition, critical factors for its implementation are identified, such as data quality, costs and staff training. The findings highlight that AI significantly improves threat detection and adaptation to new attacks. However, its adoption faces technical and economic challenges. Finally, strategies are proposed to optimize its integration into business environments, guaranteeing more efficient protection against cyber attacks. |
| eu_rights_str_mv | openAccess |
| format | bachelorThesis |
| id | UTB_8a9a5b84efff621ebbfd02fc7308ca73 |
| instacron_str | UTB |
| institution | UTB |
| instname_str | Universidad Técnica de Babahoyo |
| language_invalid_str_mv | es |
| network_acronym_str | UTB |
| network_name_str | Repositorio Universidad Técnica de Babahoyo |
| oai_identifier_str | oai:dspace.utb.edu.ec:49000/17909 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Babahoyo: UTB-FAFI. 2025 |
| reponame_str | Repositorio Universidad Técnica de Babahoyo |
| repository.mail.fl_str_mv | . |
| repository.name.fl_str_mv | Repositorio Universidad Técnica de Babahoyo - Universidad Técnica de Babahoyo |
| repository_id_str | 0 |
| rights_invalid_str_mv | Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ |
| spelling | La inteligencia artificial en la detección de intrusos en infraestructura de TI.Herrera Flores, Anthony AronCiberseguridadInteligencia ArtificialDetección de intrusosMachine LearningInfraestructuras de TIAlgoritmos de clasificaciónRedes NeuronalesSistemas de InformaciónDigitalization has turned IT infrastructures into targets of increasingly sophisticated cyber attacks, highlighting the limitations of traditional intrusion detection methods. In this context, Artificial Intelligence (AI) emerges as a key tool to improve threat identification, reduce false positives and automate incident response. This study analyzes the impact of AI in cybersecurity, comparing algorithms such as Random Forest, Neural Networks and Support Vector Machines (SVM) in intrusion detection. In addition, critical factors for its implementation are identified, such as data quality, costs and staff training. The findings highlight that AI significantly improves threat detection and adaptation to new attacks. However, its adoption faces technical and economic challenges. Finally, strategies are proposed to optimize its integration into business environments, guaranteeing more efficient protection against cyber attacks.Digitalization has turned IT infrastructures into targets of increasingly sophisticated cyber attacks, highlighting the limitations of traditional intrusion detection methods. In this context, Artificial Intelligence (AI) emerges as a key tool to improve threat identification, reduce false positives and automate incident response. This study analyzes the impact of AI in cybersecurity, comparing algorithms such as Random Forest, Neural Networks and Support Vector Machines (SVM) in intrusion detection. In addition, critical factors for its implementation are identified, such as data quality, costs and staff training. The findings highlight that AI significantly improves threat detection and adaptation to new attacks. However, its adoption faces technical and economic challenges. Finally, strategies are proposed to optimize its integration into business environments, guaranteeing more efficient protection against cyber attacks.La digitalización ha convertido a las infraestructuras de TI en blancos de ataques cibernéticos cada vez más sofisticados, evidenciando las limitaciones de los métodos tradicionales de detección de intrusos. En este contexto, la Inteligencia Artificial (IA) emerge como una herramienta clave para mejorar la identificación de amenazas, reducir falsos positivos y automatizar la respuesta ante incidentes. Este estudio analiza el impacto de la IA en ciberseguridad, comparando algoritmos como Random Forest, Redes Neuronales y Support Vector Machines (SVM) en la detección de intrusos. Además, se identifican factores críticos para su implementación, como la calidad de los datos, costos y capacitación del personal. Los hallazgos resaltan que la IA mejora significativamente la detección de amenazas y la adaptación a nuevos ataques. No obstante, su adopción enfrenta desafíos técnicos y económicos. Finalmente, se proponen estrategias para optimizar su integración en entornos empresariales, garantizando una protección más eficiente contra ciberataques.Babahoyo: UTB-FAFI. 2025Fernández Torres, Ana Del Rocío2025-04-25T16:28:31Z2025-04-25T16:28:31Z2025info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesis39 p.application/pdfhttp://dspace.utb.edu.ec/handle/49000/17909esAttribution-NonCommercial-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/info:eu-repo/semantics/openAccessreponame:Repositorio Universidad Técnica de Babahoyoinstname:Universidad Técnica de Babahoyoinstacron:UTB2025-04-29T08:10:51Zoai:dspace.utb.edu.ec:49000/17909Institucionalhttp://dspace.utb.edu.ec/Universidad públicahttps://utb.edu.ec/http://dspace.utb.edu.ec/oai.Ecuador...opendoar:02026-02-28T22:26:54.941696Repositorio Universidad Técnica de Babahoyo - Universidad Técnica de Babahoyotrue |
| spellingShingle | La inteligencia artificial en la detección de intrusos en infraestructura de TI. Herrera Flores, Anthony Aron Ciberseguridad Inteligencia Artificial Detección de intrusos Machine Learning Infraestructuras de TI Algoritmos de clasificación Redes Neuronales Sistemas de Información |
| status_str | publishedVersion |
| title | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| title_full | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| title_fullStr | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| title_full_unstemmed | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| title_short | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| title_sort | La inteligencia artificial en la detección de intrusos en infraestructura de TI. |
| topic | Ciberseguridad Inteligencia Artificial Detección de intrusos Machine Learning Infraestructuras de TI Algoritmos de clasificación Redes Neuronales Sistemas de Información |
| url | http://dspace.utb.edu.ec/handle/49000/17909 |