Leveraging Machine Learning for Anomaly Detection in Azure Security Center

Abstract

As organizations increasingly migrate their workloads to cloud platforms such as Microsoft Azure, the complexity and scale of security management also grow. One of the most significant challenges in securing cloud environments is the identification and response to security threats, particularly those that are subtle and difficult to detect using traditional methods. Anomaly detection has emerged as a powerful technique for identifying unusual patterns of behavior that could signify potential security threats. Leveraging Machine Learning (ML) for anomaly detection in the Azure Security Center offers a proactive and adaptive approach to detecting malicious activities and vulnerabilities. By training models on historical data, ML algorithms can identify deviations from typical network traffic, user behavior, and resource usage, providing real-time alerts and enabling quicker responses to potential threats. This paper explores how Azure Security Center integrates with ML for anomaly detection, discusses the challenges and benefits of using machine learning in cloud security, and offers recommendations for implementing these models to enhance Azure's security posture. By utilizing ML-driven anomaly detection, organizations can significantly improve their ability to detect and mitigate security incidents in real-time, thereby strengthening their defenses against evolving cyber threats.