AI-Driven Anomaly Detection in NoSQL Databases for Enhanced Security

Abstract

As NoSQL databases become increasingly prevalent in modern applications, their security vulnerabilities have become a growing concern. This paper presents an innovative approach to anomaly detection in NoSQL databases, leveraging advanced artificial intelligence (AI) techniques to enhance security measures. The proposed framework utilizes machine learning algorithms, particularly unsupervised learning methods, to identify unusual patterns of database access and modifications that may indicate potential security breaches. By analyzing historical transaction data and real-time access logs, the AI model effectively distinguishes between normal and anomalous behavior, providing timely alerts for suspicious activities. The effectiveness of the proposed framework is evaluated using various performance metrics, including accuracy, precision, recall, and F1-score, demonstrating its capability to achieve high detection rates with low false positives. Additionally, we explore the practical implications of implementing AI-driven anomaly detection in NoSQL databases and discuss challenges and future directions for research. This work highlights the critical role of AI in fortifying database security, contributing to more resilient data management solutions in an era of increasing cyber threats.