Tendencias de las técnicas de la inteligencia artificial, en la detección de delitos informáticos: Revisión Sistemática de la Literatura (SLR)
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En este artículo, se presenta una Revisión Sistemática de la Literatura (RSLR) que se centra en las aplicaciones de las técnicas de Inteligencia Artificial (IA) con el propósito de detectar delitos informáticos. La primera parte de la revisión se dedica a seleccionar las fuentes de información a emplear, mientras que en la siguiente sección se proporciona una descripción detallada de las investigaciones que han empleado estas técnicas de (IA). Durante esteel proceso de investigación, se evidenció que la mayoría de los estudios han empleado una diversidad de algoritmos de (IA), e. Entre los más frecuentes figuran SVM, Decision Tree, Logistic Regression, Naive Bayes, KNN y Random Forest, los cuales han demostrado su eficacia en múltiples áreas de ciberseguridad, abarcando la detección de intrusiones, ataques de denegación de servicio (DoS), phishing y malware. En este contexto, se ha observado que XGBoost, Random Forest y Logistic Regression destacan por su asombroso equilibrio entre las métricas de precisión y exactitud, como lo respaldan varias investigaciones, . lLos hallazgos enfatizan la necesidad de adaptar la elección del algoritmo según el conjunto de datos y el contexto específico, subrayando la importancia de llevar a cabo pruebas y definiciones meticulosas. Por último, los resultados obtenidos de esta revisión proporcionan una guía esclarecedora que puede orientar decisiones, ofreciendo a los lectores una visión de las técnicas más prometedoras de las áreas que ameritan mayor atención, además de la exploración para futuras investigaciones.
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