Machine Learning for Threat Detection: From Predictive Analytics to Proactive Defense

Abstract

Because of the continuous change in cyber threats, companies are seriously considering integrating machine learning (ML) algorithms into their protective measures. This article elaborates on the function of ML in threat detection, particularly its significant impact on the shift from classical predictive analytics to proactive defense approaches. By considering data from real-world case studies and incorporating some ML approaches, the research investigates the capability of ML to predict, detect, and block cyber threats, which can inflict heavy damage, before they occur. The primary deductions are that ML technology will increase the capacity to spot threats much earlier and stop them instantly, resulting in a stronger and quicker organizational response to ever-changing cyber-attacks. The paper adds to the academic recognition of how ML impacts the security of information systems. It provides insight into how advanced ML models could be integrated into existing security frameworks to promote proximal defense mechanisms.