Machine Learning Technique for Anomaly Detection

Abstract

Anomaly detection is an essential component of storage monitoring systems, allowing irregular patterns, deviations, and outliers to be identified. The objective of the project is to design and develop an iannovative storage monitoring system empowered by machine learning techniques to enable real-time anomaly detection, proactive management, and efficient utilization of storage infrastructure resources. This tool aims to continuously monitor diverse storage metrics, identify abnormal patterns or behaviors, and provide actionable insights for administrators to maintain optimal storage performance, minimize downtime and enhance data integrity across various storage systems. The key focus is leveraging advanced analytics to predict, detect, and address potential anomalies, ensuring the reliability, efficiency, and security of storage operations within dynamic and data-intensive environments.