Abstract
With the growing complexity of modern cyber threats, proactive defense has become essential for securing digital infrastructures. Machine Learning (ML) models offer powerful capabilities for predicting, classifying, and mitigating cyberattacks before they occur. This research paper presents an analysis of key ML algorithms used in cybersecurity, discusses a predictive threat-mitigation framework, and evaluates model performance using comparative metrics. A sample dataset is examined to demonstrate how machine learning models can anticipate attack patterns, enabling organizations to strengthen defensive postures through automated, data-driven insights.
