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 contribución | Bibliometric analysis on the use of Machine Learning in cybersecurity |
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Idioma original | Español |
Páginas (desde-hasta) | 60-72 |
- | 13 |
Publicación | RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao |
Volumen | 2023 |
N.º | E62 |
Estado | Indizado - 2023 |
Publicado de forma externa | Sí |
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