Neural Networks for Database Anomaly Detection in SQL Server

Abstract

Database anomaly detection is a crucial aspect of database management, security, and performance optimization. Traditional approaches for detecting anomalies in SQL Server databases typically rely on rule-based or statistical methods, which can struggle to identify complex, non-linear patterns or adapt to evolving data. Neural networks, with their ability to learn from vast amounts of data and recognize intricate patterns, offer a promising alternative for database anomaly detection. This paper explores the application of neural networks in detecting anomalies within SQL Server databases, highlighting their potential for identifying performance issues, security breaches, and data corruption. It covers the types of neural networks suitable for anomaly detection, including feedforward networks, recurrent neural networks (RNNs), and autoencoders. Furthermore, the paper discusses the architecture, challenges, and benefits of integrating neural networks with SQL Server for real-time anomaly detection, and provides examples of how these models can be utilized in various real-world scenarios such as fraud detection, performance monitoring, and data integrity.