Optimizing Cloud Security with Machine Learning: Predicting and Preventing Vulnerabilities in Distributed Systems

Cloud infrastructures have immense potential with unique security threats due to its distributed design and the dynamic nature of threats. This study examines ways machine learning (ML) could improve security through predictions of, and protection against, vulnerabilities in complicated systems. Among five algorithms-Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Isolation Forest-compared on accuracy, precision, recall, and F1-score, Random Forest shows the highest performance (approximately 95 % accuracy, 96 % precision, 94 % recall, and a 95% F1-score). In comparison to this, Isolation Forest shows lower performance on all aspects. The proposed architecture integrates supervised, unsupervised, and reinforcement learning techniques into existing cloud security pipelines to enable real-time threat detection, remediation, and continuous learning. Not only does this enhance defense against new and sophisticated attacks, but it also reduces false positives and incident response times. As cloud infrastructures grow in size and complexity, the incorporation of security measures based on ML is imperative to provide a strong and secured environment.