Automated Anomaly Detection in Database Management Systems Using Machine Learning

Abstract

The ability to detect anomalies in database management systems (DBMS) is critical for ensuring data integrity, security, and operational efficiency. This paper explores the application of machine learning techniques for automated anomaly detection in DBMS. We discuss various machine learning algorithms, their effectiveness in identifying deviations from normal database behavior, and propose a framework for integrating these methods into existing database systems.