Drift Detection and Model Update using Unsupervised AutoML in IoT

Abstract

This paper addresses the challenges of concept drift on the Internet of Things (IoT) environments and evaluates a machine-learning model's performance under varying data drift conditions using unsupervised Automatic Machine Learning (AutoML) anomaly detection techniques. By implementing a dynamic learning framework and employing advanced analytics, the study showcases the resilience of the proposed methodology against evolving data patterns. The results demonstrate the model's robust predictive capabilities, even in high drift scenarios, underscoring the importance of adaptive models in maintaining effective IoT security measures. The achieved improvement percentages can reach 46% for the F1 score.