Tendencias investigativas en el uso de Machine Learning en la ciberseguridad

Diana Marleny Ramírez Ramírez, Luis Fernando Garcés-Giraldo, Teddy Doria-Orozco, Sebastián Franco-Castaño, Alejandro Valencia-Arias, Paula Andrea Rodríguez-Correa, Jenner Espinoza Román

Producción científica: Artículo CientíficoArtículo originalrevisión exhaustiva

Resumen

The increase in online data has generated the need to protect user privacy through cybersecurity. Machine learning is used to detect intruders, but there are limitations. Therefore, the goal is to investigate research trends on the use of Machine Learning in cybersecurity in the past five years. The article uses bibliometric analysis to evaluate scientific activity on the use of Machine1Learning in cybersecurity, employing PRISMA 2020 criteria on Scopus and Web of Science databases. The main results reflect growth in the subject between 2021 and 2022, with the United States and China as the leading research countries. Discussions cover main topics such as Deep Learning and intrusion detection, as well as emerging words such as LSTM and multi-agent systems. The conclusion emphasizes the need for future research in these areas.

Título traducido de la contribuciónBibliometric analysis on the use of Machine Learning in cybersecurity
Idioma originalEspañol
Páginas (desde-hasta)60-72
-13
PublicaciónRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
Volumen2023
N.ºE62
EstadoIndizado - 2023
Publicado de forma externa

Nota bibliográfica

Publisher Copyright:
© 2023, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Palabras clave

  • Deep Learning
  • Intrusion detection
  • PRISMA-2020
  • Research agenda
  • User privacy

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