Anomaly Detection in Azure Cloud Infrastructure Using Machine Learning

Abstract

Cloud infrastructure generates a continuous, high-volume stream of telemetry, spanning resource utilization metrics, application performance traces, network flow data, and system logs, within which genuine failures and security incidents are rare events buried in a much larger volume of normal variation. Rule-based thresholding, the traditional approach to detecting infrastructure problems, struggles to keep pace with this volume and with workloads whose normal behavior shifts seasonally or evolves gradually over time. Machine learning offers a more adaptive alternative, learning the statistical or temporal structure of normal infrastructure behavior and flagging deviations without requiring every failure mode to be anticipated in advance. This article examines how machine learning techniques can be applied to detect anomalies in Microsoft Azure cloud infrastructure. The article proposes a machine learning pipeline spanning telemetry collection through Azure Monitor and Log Analytics, feature engineering, model-based anomaly scoring, and automated alerting through Azure Monitor and Sentinel, compares six machine learning techniques against the Azure infrastructure signals each is best suited to, and maps common categories of infrastructure anomaly to specific recommended techniques. Two tables and one pipeline architecture figure illustrate this analysis. The findings indicate that no single technique addresses every anomaly category; isolation-based methods suit high-dimensional resource metrics, autoencoder and variational approaches suit temporal and seasonal patterns, and transformer-based models suit unstructured log data, meaning a production Azure anomaly detection system typically requires a combination of techniques rather than a single model. The article concludes with practical recommendations for implementing machine-learning-based anomaly detection in Azure environments.