Modelos de Inteligencia Artificial para el Análisis de Eventos de Registro
DOI:
https://doi.org/10.29352/mill0220e.41569Palabras clave:
inteligência artificial; detección de anomalias; logs; ciberseguridad; machine learningResumen
En el campo de la ciberseguridad, el análisis de registros (logs) desempeña un papel fundamental en la identificación de patrones, anomalías y actividades potencialmente maliciosas en redes informáticas, permitiendo respuestas proactivas y bien fundamentadas. Este artículo tiene como objetivo explorar y comparar diversos modelos de Inteligencia Artificial orientados a la detección de anomalías en eventos de registro, con énfasis en su aplicación en redes institucionales. El estudio se basa en una revisión exhaustiva de la literatura y en el análisis de modelos de Aprendizaje Automático (Machine Learning) y Aprendizaje Profundo (Deep Learning), tanto supervisados como no supervisados. Se consideraron factores como la sensibilidad a los patrones de anomalía y el uso de recursos computacionales. Los resultados evidencian la diversidad de estos modelos en cuanto a características y contextos de aplicación, destacando su adaptabilidad. Esta revisión sistemática proporciona una base sólida para apoyar la toma de decisiones frente a los desafíos relacionados con el análisis de grandes volúmenes de datos. Finalmente, la investigación ofrece un punto de partida para la selección de modelos adecuados, que serán evaluados en entornos operativos.
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