Leveraging Machine Learning For Automated Anomaly Detection In Cloud Infrastructures

Abstract

The article introduces an automated method of machine learning to detect anomalies in cloud infrastructures in order to deal with the increasing complexity and security issues of contemporary distributed computing systems. Since conventional threshold-based monitoring is becoming progressively ineffective in identifying subtle, multidimensional anomalies in dynamic cloud ecosystems, the integration of machine learning techniques is a promising solution. The article includes a detailed collection of data throughout the infrastructure levels, creation of feature engineering to describe the patterns of system behavior, and the application of ensemble detection algorithms that detect complex anomalies and stop the service disruption before it takes place. Using the principle of event-driven design and serverless elements, the architecture allows building scalable and resilient monitoring features with automated alerting and remediation features. Experimental findings show that there are considerable advances in detection accuracy, incidence reaction duration, and operation effectiveness, compared to traditional surveillance strategies, and implementation difficulties are recognized, such as model drift, addressing false positive issues, and specialized aptitude demands.