AN EXAMINATION OF THE ROLE OF MACHINE LEARNING IN CYBER SECURITY AND THE ASSESSMENT OF ITS ABILITIES FOR DETECTING THREATS

Abstract

The ground breaking implications of ML for cybersecurity are explored in this work, especially in regard to its function beyond the conventional threat detection. Traditional approaches often fail when confronted with dynamic cyber threats. The use of machine learning to analyse large datasets and identify patterns has the potential to greatly improve cybersecurity measures. The machine learning methods discussed in this article have the potential to be used in several fields, such as automated response, threat prediction, and anomaly detection. How ML models can track attack patterns over time and spot tiny warning signs that people may miss is the subject of this investigation. The research also delves into the potential applications of ML in real-time reaction systems, which can adapt to evolving threats by absorbing fresh data in real-time. The research emphasizes that ML can automate mundane security jobs, improve threat intelligence, and optimize resource allocation, in addition to detection and response. Organizations may be able to achieve more proactive and adaptable security postures by integrating ML into cybersecurity frameworks. Methods like ML-based behavioural analysis may help shed light on user actions and highlight suspicious patterns that may indicate security holes. In the final portion of the paper, the possibilities of machine learning to transform cybersecurity procedures are discussed at length. It shows that technology can do more than just identify threats; it can also respond to them, forecast their moves, and manage security as a whole. Additional flexible and safe cybersecurity systems might emerge in the future, thanks to the results that could open the door to additional ML studies and applications.