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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-08436-x |
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| Summary: | 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, and standard metrics. Logistic Boosting outperformed other models with an AUC of 0.992 (96.6% accuracy, 93.5% precision, 94.8% recall, F1-score = 0.941), demonstrating superior handling of imbalanced data (134 FPs, 117 FNs). While Random Forest achieved strong results (AUC = 0.982) and SVM showed high recall, Logistic Boosting’s ensemble approach proved most effective for industrial IoT classification. The findings provide actionable insights for real-time detection systems and suggest future directions in hybrid architectures and edge optimization. |
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| ISSN: | 2045-2322 |