Machine Learning for Cloud Security Metrics.pdf

The growing complexity of cloud environments has increased the need for advanced security mechanisms to detect and mitigate cyber threats effectively. Machine learning (ML) has emerged as a transformative approach in cloud security, enabling organizations to analyze vast amounts of security data, identify patterns, and automate threat response mechanisms. This paper explores how ML-driven security metrics enhance threat detection, improve incident response, and strengthen overall cloud security. Key ML-based security metrics, such as anomaly detection rates, false positive reduction, and response time optimization, are examined. Furthermore, established frameworks for integrating ML into cloud security, including supervised and unsupervised learning models, deep learning techniques, and reinforcement learning, are discussed. The paper concludes with best practices for deploying ML-driven security solutions, ensuring compliance, and mitigating cyber risks in cloud environments.