Enterprise AI Analysis
SHAP enhanced transformer GWO boosting model for transparent and robust anomaly detection in IIoT environments
This study introduces a hybrid AI framework for anomaly detection in Industrial IoT (IIoT) environments, combining a temporal transformer encoder, Grey Wolf Optimizer (GWO) for feature selection, and Logistic Boosting classifier. The model achieves 98.2% accuracy, 96.7% precision, 97.1% recall, and an F1-score of 0.969 on a six-month real-world dataset. It demonstrates superior robustness against data drift, maintaining high performance with only 3.2% accuracy degradation under maximum drift conditions, significantly outperforming traditional and deep learning baselines. The framework also provides SHAP-based explainability, ensuring transparent anomaly alerts and aiding root-cause analysis for industrial operators, making it suitable for secure and adaptive industrial systems.
Executive Impact: Key Performance Metrics
Our proposed AI model sets a new benchmark for anomaly detection in IIoT, delivering unparalleled accuracy and robustness, crucial for maintaining operational integrity and safety.
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Deep Analysis & Enterprise Applications
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Anomaly detection is critical for maintaining the reliability and safety of Industrial IoT (IIoT) systems. This paper introduces a novel hybrid framework that addresses the challenges of temporal dependencies, data imbalance, and concept drift, while providing transparent insights for operational decision-making.
98.2%
Accuracy Achieved
3.2%
Accuracy Degradation Under Drift
Integrated Anomaly Detection Pipeline
Data Preprocessing
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Temporal Encoding (Transformer)
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Feature Optimization (GWO)
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Logistic Boosting Classifier
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Anomaly Detection & Alerts
| Model | Accuracy | F1-Score | Robustness | Interpretability | Edge Deployability |
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| TGB (Proposed) | 98.2% | 0.969 |
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| ITran (Transformer) | 97.4% | 0.961 |
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| Logistic Boosting | 96.6% | 0.952 |
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| LSTM-AE | 96.1% | 0.946 |
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| SVM (RBF) | 95.2% | 0.931 |
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| Random Forest | 94.8% | 0.928 |
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Transparent Anomaly Reasoning with SHAP
The SHAP analysis reveals that Power Consumption and Motion Detection are the most influential features, followed by Ambient Temperature and Light Intensity. This transparency allows operators to quickly understand the root cause of anomalies and trust the system's decisions.
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