Machine Learning Methods for Cybersecurity: Review and Bibliometric Analysis
Eszter Kail, Anna Bánáti, Rita Fleiner, Amirhosein Mosavi, Csaba Makó · 2023
This article presents a bibliometric analysis and review of the applications of machine learning methods in cybersecurity. A taxonomy of the methods and applications is presented, and fundamental studies are introduced and reviewed. The taxonomy of the methods classifies the machine learning methods in four fundamental groups based on the methods' architecture, i.e., artificial neural networks-based, decision trees-based, support vectors machines-based, and ensembles. The specific cybersecurity application of each group is further discussed. The deep learning and hybrid methods are not included in this study. The bibliometric analysis is performed considering the Scopus database. For selection and evaluation of the articles a modified version of PRISMA is proposed.