Mohamed Khalafalla Hassan, Ibrahim Yousif Alshareef, "Drift Detection and Model Update using Unsupervised
AutoML in IoT," WSEAS Transactions on Computers, vol. 22, pp. 332-337, 2023, DOI:10.37394/23205.2023.22.38
Mohamed Khalafalla Hassan, Ibrahim Yousif Alshareef. Drift Detection and Model Update using Unsupervised
AutoML in IoT.
WSEAS Transactions on Computers. 2023;22:332-337. 10.37394/23205.2023.22.38
WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 22, 2023
Drift Detection and Model Update using Unsupervised
AutoML in IoT
Authors: ,
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.
Pages: 332-337 WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 22, 2023, Art. #38 DOI: 10.37394/23205.2023.22.38
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