Enhancing Cybersecurity in Industrial Internet of Things Systems Using Ensemble Learning Against False Data Injection Attacks
DOI:
https://doi.org/10.59796/jcst.V16N1.2026.151Keywords:
industrial internet of things, false data injection attack, ensemble learning, weighted voting, intrusion detection, cybersecurity, random forest, XGBoostAbstract
False Data Injection Attacks (FDIAs) pose critical threats to Industrial Internet of Things (IIoT) systems by manipulating sensor data to cause operational disruptions and safety hazards. Traditional intrusion detection systems struggle to identify the subtle anomalies characteristic of FDIAs, necessitating advanced machine learning approaches. This study develops a weighted voting ensemble framework integrating Random Forest, XGBoost, Neural Network, and Logistic Regression with F1-score-based dynamic weight assignment for optimized FDIA detection. The proposed ensemble was evaluated on the UKMNCT_IIoT_FDIA dataset containing 15,425 instances across 30 features. Using 70–30 train–test split, model performance was assessed through accuracy, precision, recall, F1-score, and confusion matrix analysis. Results demonstrate exceptional performance: 99.71% accuracy, 99.72% precision, 99.72% recall, and 99.72% F1-score. Confusion matrix analysis revealed only 2 false negatives and 9 false positives across 4,627 test instances, substantially outperforming individual classifiers while maintaining computational efficiency suitable for resource-constrained edge devices.
The weighted voting mechanism successfully leverages algorithmic diversity to achieve superior robustness compared to individual models. Tree-based ensembles (Random Forest: 99.74%, XGBoost: 99.68%) substantially outperformed Neural Network (87.14%) and Logistic Regression (83.32%), confirming the importance of non-linear modeling for complex attack pattern detection. The minimal false negative rate (0.04%) represents critical advancement for critical infrastructure protection where undetected attacks carry severe consequences. This research establishes the efficacy of performance-adaptive ensemble learning for IIoT cybersecurity, providing a practical, scalable solution for safeguarding industrial cyber-physical systems against evolving threats.
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