da3a55e6-486a-4700-a590-4a9647565de420240226062814594wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTERS2224-28721109-275010.37394/23205http://wseas.org/wseas/cms.action?id=40261920231920232210.37394/23205.2023.22https://wseas.com/journals/computers/2023.phpMohamed KhalafallaHassanUniversity Technology Malaysia, MALAYSIAIbrahim YousifAlshareefFaculty of Telecommunication Engineering, Future University, SUDANThis 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.123120231231202333233738https://wseas.com/journals/computers/2023/a785105-035(2023).pdf10.37394/23205.2023.22.38https://wseas.com/journals/computers/2023/a785105-035(2023).pdf10.3390/s23135941E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, "CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment", Sensors 2023, 23, 5941. https://doi.org/10.3390/s23135941. 10.1007/s42452-019-1925-yS. Selvaraj and S. Sundaravaradhan, "Challenges and opportunities in IoT healthcare systems: a systematic review," SN Applied Sciences, vol. 2, no. 1, p. 139, 2020. 10.3390/fi11040094F. Zantalis, G. Koulouras, S. Karabetsos, and D. 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