Revisión sistemática sobre identificación de anomalías en el consumo de servicios web masivos mediante técnicas de machine learning
DOI:
https://doi.org/10.24054/rcta.v1i47.4356Palabras clave:
revisión sistemática, detección de anomalías, machine learning, QoS, servicios webResumen
Este artículo presenta una revisión sistemática de la literatura sobre el uso de técnicas de Machine Learning aplicadas a la identificación de anomalías en servicios web y sistemas distribuidos. El proceso de revisión se desarrolló a partir de búsquedas estructuradas en bases de datos académicas reconocidas, incluyendo IEEE Xplore, Scopus, ScienceDirect y ACM Digital Library, considerando publicaciones entre 2021 y 2025. Se aplicaron criterios explícitos de inclusión y exclusión, lo que permitió seleccionar un conjunto final de cincuenta artículos relevantes. Los estudios analizados se organizaron según tipo de dato, enfoque de aprendizaje, dominio de aplicación y métricas empleadas, con el fin de identificar tendencias, fortalezas y limitaciones del estado del arte. Los resultados evidencian una creciente adopción de modelos híbridos y arquitecturas profundas, así como un interés sostenido por la explicabilidad y la escalabilidad en entornos distribuidos.
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Derechos de autor 2026 Jesús Andrés Cruz Sanabria, Joaquín Iván Barrera Lozada, Karla Yohana Sánchez Mojica
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
