Cybersecurity Frameworks Enhanced by Machine Learning Techniques

Abstract

The ever-evolving landscape of cyber threats necessitates the integration of advanced technologies into cybersecurity frameworks. This research paper explores the enhancement of traditional cybersecurity frameworks through the application of machine learning (ML) techniques. We examine various ML methodologies, including supervised learning, unsupervised learning, and deep learning, and their roles in improving key cybersecurity functions such as threat detection, incident response, and vulnerability management. By analyzing existing literature and case studies, we evaluate the effectiveness of ML-enhanced frameworks in detecting anomalous behaviors, predicting potential attacks, and automating response actions. Our findings indicate that machine learning significantly enhances the capability of cybersecurity frameworks to adapt to new and sophisticated threats, improving accuracy and reducing response times. However, challenges such as data quality, algorithmic bias, and the need for interpretability in decision-making processes remain critical concerns. This paper concludes with recommendations for organizations to adopt ML-driven frameworks while emphasizing the importance of human oversight and continuous learning to ensure effective cybersecurity posture in an increasingly complex threat environment.