Bibliometric analysis on the use of Machine Learning in cybersecurity

Ramírez D.M.R.; Garcés-Giraldo L.F.; Doria-Orozco T.; Franco-Castaño S.; Valencia-Arias A.; Rodríguez-Correa P.A.; Román J.E. · RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao (2023)

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.

Instituciones: Universidad Ricardo Palma; Universidad de San Martín de Porres