An Analysis of Machine Learning Methods used in Cyber Security during the Last Decade
R Babu, Tarun Dhar Diwan, Ankit Vishnoi, P. William, Parul Madan, Keshav Kaushik
Abstract
This paper examines cybersecurity machine learning (ML) methods over the last decade. In this study, we examine how cyberthreats have evolved and how machine learning has progressed to combat them. Each problem is well discussed. Both topics are being discussed extensively. Cybersecurity has long employed several machine learning methods. This category includes reinforcement, supervised, and unsupervised learning methods. Other teaching methods include unsupervised learning. These methods excel in identifying possible threats. This article provides an overview of machine learning (ML)-based cybersecurity solutions to highlight major topics, issues, and research opportunities. The study uses academic, commercial, and real-world data to do this. It highlights important issues, difficulties, and opportunities.