Abstract
Cybersecurity threats present significant challenges in the ever-evolving landscape of information and communication technology (ICT). As a practical approach to counter these evolving threats, corporations invest in various measures, including adopting cybersecurity standards, enhancing controls, and leveraging modern cybersecurity tools. Exponential development is established using machine learning and artificial intelligence within the computing domain. Cybersecurity tools also capitalize on these advancements, employing machine learning to direct complex and sophisticated cyberthreats. While incorporating machine learning into cybersecurity is still in its preliminary stages, continuous state-of-the-art analysis is necessary to assess its feasibility and applicability in combating modern cyberthreats. The challenge remains in the relative immaturity of implementing machine learning in cybersecurity, necessitating further research, as emphasized in this study. This study used the preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology as a scientific approach to reviewing recent literature on the applicability and feasibility of machine learning implementation in cybersecurity. This study presents the inadequacies of the research field. Finally, the directions for machine learning implementation in cybersecurity are depicted owing to the present study’s systematic review. This study functions as a foundational baseline from which rigorous machine-learning models and frameworks for cybersecurity can be constructed or improved.