Resumen

QUIRUMBAY YAGUAL, Daniel Ivan; CASTILLO YAGUAL, Carlos Andrés  y  CORONEL SUAREZ, Iván Alberto. A review of deep learning applied to cybersecurity. RCTU [online]. 2022, vol.9, n.1, pp.57-65. ISSN 1390-7697.  https://doi.org/10.26423/rctu.v9i1.671.

This study presents an overview on cybersecurity from the perspective of neural networks and deep learning techniques according to the various current needs in computer security environments. It discusses the applicability of these techniques in various cybersecurity works, such as intrusion detection, malware or botnet identification, phishing, cyber attack prediction, denial of service, cyber anomalies, among others. For this study, the analytical-synthetic method was applied to identify optimal solutions in the field of cybersecurity. The results highlight and recommend algorithms applicable to cybersecurity as a knowledge base and facility for future research within the scope of this study in the field. This research serves as a reference point and guide for academia and practitioners in cyber security industries from the deep learning point of view.

Palabras clave : deep learning; internet of things; artificial intelligence; neural networks; cyber security.

        · resumen en Español     · texto en Español     · Español ( pdf )