Systematic Literature Review on Automatic Anomaly Detection Based on Database Logs

Automatic anomaly detection based on database logs is an approach for ensuring the security and reliability of modern database management systems. This systematic literature review, which is significant to the field, summarises the existing research on database logs methodologies, algorithms, and anomaly detection applications. The review explores various approaches, including statistical methods, machine learning techniques, and hybrid models, highlighting their effectiveness in identifying anomalies such as unauthorised access, data corruption, and performance issues. By analysing conference papers, articles, and industry reports published over the past two decades, this study identifies key trends, challenges, and future directions in the field. The findings suggest that while significant advancements have been made, there are still gaps in scalability, real-time detection, and handling of complex data environments. This review provides a comprehensive overview for researchers and practitioners, engaging them in the current landscape and inspiring them to develop more robust anomaly detection systems in database management.