Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach

Abstract Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. A real-world dataset of 15,000 instances from factory sensors was analyzed using ROC curves, confusion matrices,...

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Bibliographic Details

Published in:	Scientific Reports
Main Authors:	Mohammed Aly, Mohamed H. Behiry
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-07-01
Subjects:	Anomaly detection IoT Machine learning Logistic boosting Industrial cybersecurity Imbalanced data
Online Access:	https://doi.org/10.1038/s41598-025-08436-x