Enhancing Cyber Security Through Machine Learning: A Comprehensive Approach to Intelligent Threat Detection and Prevention

The rapid growth of digital technologies and interconnected systems has significantly increased cyber security risks worldwide. Traditional security mechanisms often struggle to detect sophisticated cyber threats due to the evolving nature of attacks. Machine Learning (ML) has emerged as a powerful solution for enhancing cyber security by enabling automated threat detection, anomaly identification, malware classification, and intrusion prevention. This paper examines the role of machine learning in strengthening cyber security frameworks, evaluates different ML algorithms used in cyber defense, and presents a comparative analysis of their effectiveness. The findings suggest that machine learning-based approaches significantly improve detection accuracy while reducing response times compared to conventional security methods.