Machine Learning for Cybersecurity: A Bibliometric Analysis from 2019 to 2023

With the increasing breadth and sophistication of cyber threats, machine learning must be integrated into cybersecurity. This study uses a bibliometric analysis on 427 documents from 2019 to 2023 to pinpoint current trends in machine learning applications for cybersecurity. An exponential 96.55 percent yearly growth in publications is shown by our analysis of data from the SCOPUS database, indicating a spike in research activity. We identify eminent journals like IEEE Access that are leading the way in dissemination, and active contributors like Sarker IH. Key research themes, including malware detection, Internet of Things ecosystems, network security, and model accuracy optimization, are revealed through the examination of keywords and semantic topics. The adoption of deep learning is a sign of technological progress. According to our findings, machine learning integration is widely used in cybersecurity for tasks like threat intelligence and infrastructure monitoring, to name just two. Availability improvements are still given priority by reliable automation. Due to the increase in cyber threats, machine learning is becoming a necessary skill rather than an add-on. This research sheds light on the groundbreaking findings and exponential trajectory that are transforming machine learning's application in cybersecurity. Machine learning is ushered into a new era of intelligent and flexible cyber defense systems, which implies that sustained innovation through international cooperation will be crucial. Our timely bibliometric analysis establishes a framework for future cybersecurity and machine learning research, as well as for technological advancement.