Utilizing Machine Learning and Data Analytics for Improving Cybersecurity Risk Management and Assessment

Abstract

This paper presents a systematic review on the use of machine learning (ML) and data analytics in improving cybersecurity risk management and assessment. With the increasing sophistication and frequency of cyberattacks, traditional cybersecurity strategies are no longer sufficient to address the growing threats. The integration of ML and data analytics offers promising solutions for proactive threat detection and risk mitigation. Through a comprehensive analysis of existing research, this review identifies key applications of predictive analytics, supervised learning algorithms, and anomaly detection techniques in cybersecurity. The findings highlight the potential of these technologies to enhance the accuracy and efficiency of cybersecurity measures, but also emphasize the challenges related to data quality, adversarial attacks, model scalability, and privacy concerns. The paper concludes by outlining the future resear