AI-Driven Anomaly Detection in Network Monitoring Techniques and Tools

Abstract

Effective network monitoring is crucial for maintaining performance and security. Traditionally, tools use threshold-based methods for anomaly detection but struggle to detect complex patterns in modern dynamic networks. This paper investigates leveraging machine learning to augment monitoring capabilities. Key network monitoring tools are described along with how they currently handle anomaly detection. Machine learning techniques for developing predictive models from historical data are then discussed. A framework for integrating trained models as add-ons to existing tools is proposed. These AI-driven approaches are shown to provide more accurate and automated anomaly detection compared to legacy techniques.