A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments

A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments

Abstract Imbalanced datasets in Industrial Internet of Things (IIoT) environments pose a serious challenge for reliable pattern classification. Critical instances of minority classes (such as anomalies or system faults) are often vastly outnumbered by routine data, making them difficult to detect. T...

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Bibliographic Details
Main Authors: Rubina Riaz, Guangjie Han, Kamran Shaukat, Naimat Ullah Khan, Hongbo Zhu, Lei Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Industrial internet of things
Data augmentation
Deep learning
SMOTE
Machine learning for IIoT
Synthetic data generation
Online Access:https://doi.org/10.1038/s41598-025-07533-1
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https://doi.org/10.1038/s41598-025-07533-1

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