Machine Learning in Cybersecurity: Enhancing Threat Detection and Response

Abstract

In the rapidly evolving landscape of cybersecurity, the incorporation of machine learning (ML) technologies has emerged as a pivotal strategy for enhancing threat detection and response capabilities. This paper investigates the multifaceted role of machine learning in addressing contemporary cybersecurity challenges. It delves into various algorithms, frameworks, and technologies that facilitate real-time anomaly detection, threat identification, and vulnerability prediction. Additionally, the paper addresses the complexities associated with integrating machine learning into existing cybersecurity infrastructures, offering practical recommendations to optimize threat intelligence, automate response strategies, and bolster overall security posture. Through this exploration, we aim to provide insights into the transformative potential of machine learning in safeguarding digital environments.