Introduction to Machine Learning in Cybersecurity

Abstract

The ever-evolving landscape of cyber threats has driven the need for more sophisticated
and proactive cybersecurity measures. Traditional methods are increasingly challenged
by the speed and complexity of modern attacks. In this context, Machine Learning (ML)
has emerged as a vital tool in the cybersecurity arsenal, enabling dynamic and intelligent
threat detection and response mechanisms. This paper examines the integration of ML
in cybersecurity, highlighting its applications, advantages, and the associated challenges.
We analyze real-world case studies to demonstrate how ML is being used to detect and
mitigate cyber threats effectively. Furthermore, the paper discusses the future
prospects of ML in cybersecurity, including emerging trends and potential regulatory
implications. The insights provided here are aimed at helping stakeholders understand
the transformative role of ML in protecting digital infrastructure and data in an
interconnected world.