Challenges in Applying ML to Cybersecurity

Abstract

Machine Learning (ML) has emerged as a transformative technology in cybersecurity,
offering advanced capabilities for threat detection, anomaly detection, and incident
response. However, the application of ML to cybersecurity is fraught with challenges that
can impact its effectiveness and reliability. These challenges include issues related to data
quality, model interpretability, adversarial attacks, scalability, and the dynamic nature of
cyber threats. This article explores the multifaceted challenges associated with applying
ML to cybersecurity, providing insights into the inherent complexities and offering
strategies for overcoming these obstacles. Through a comprehensive analysis of realworld examples and theoretical considerations, we aim to highlight the critical issues and
propose actionable solutions for enhancing the integration of ML technologies into
cybersecurity practices.