A Robust Anomaly Detector for Imbalanced IIOT Data

Abstract Machine Learning (ML) and Deep Learning (DL) have been used for anomaly detection in Industrial Internet of Things (IIoT) environments. The presence of imbalanced data, high noise levels, missing values, and high dimensionality poses an enormous challenge for existing methods, leading to inconsistent reliability in detecting anomalies in real-world industrial environments. Current anomaly detection solutions suffer from high false-negative rates due to class imbalance and noisy sensor data, limiting their practical applicability. This paper proposes the Ensemble Wasserstein Generative Adversarial Network for IIoT (EWAD-IIoT) framework, which is uniquely designed to address these challenges. The aim is to build a robust anomaly detection model with high recall (94.7%) and precision (93.6%) while minimizing miss rates in complex IIoT settings. Evaluations on two benchmark datasets, SECOM (industrial sensor data) and MNIST (image data), demonstrate EWAD-IIoT’s superiority over traditional methods like standalone WGAN and WGAN-GP. To highlight its efficacy, we compare results against these benchmarks, showcasing improvements in F1-score (95.8%) and noise robustness. The framework leverages advanced preprocessing (Z-score filtering, Min-Max scaling), SMOTE-based balancing, and WGAN-generated synthetic samples to handle data imbalance and dimensionality. The results validate EWAD-IIoT’s capability to detect rare anomalies in IIoT environments, with a balanced trade-off between recall and precision, making it a scalable solution for predictive maintenance and fault diagnosis.