AN INVESTIGATION ON THE FUNCTION OF MACHINE LEARNING IN CYBER DEFENCE AND THE IDENTIFICATION OF ITS CAPABILITIES BEYOND THREAT DETECTION

Abstract

Particularly in relation to its role beyond the traditional threat detection, this study investigates the game-changing implications of ML for cybersecurity. In the face of ever-evolving cyber threats, conventional methods often fall short. By studying massive information and spotting patterns, machine learning might substantially enhance cybersecurity methods. This paper focusses on ML techniques that might be useful in several domains, including threat prediction, anomaly detection, and automated response. Investigated are the ways in which ML models can analyse attack trends over time and identify subtle indicators of impending assaults that humans might overlook. The study also explores the use of ML in real-time response systems, which may adjust to new dangers by learning from new data as it happens. In addition to detection and reaction, the paper highlights that ML can automate routine security tasks, enhance threat intelligence, and optimise resource allocation. Incorporating ML into cybersecurity frameworks may help organisations attain more proactive and flexible security postures. One of these approaches is ML-based behavioural analysis, which provides insight into user conduct and calls attention to anomalies that can suggest security flaws. Machine learning's potential to revolutionise cybersecurity processes is explored extensively in the study's last section. Beyond only detecting threats, it demonstrates how technology may provide novel solutions for prediction, response, and overall security management. The findings may pave the way for future ML research and applications, which might lead to more adaptable and secure cybersecurity systems.