Ensemble learning for real-time anomaly detection and predictive maintenance in smart factories

Abstract

This paper explores the use of several ensemble learning algorithms Gradient Boosting, XGBoost, LightGBM, Bagging, AdaBoost, and Voting Classifier on the CICIoT2023 dataset within the framework of Industrial Internet of Things (IIoT) and Intrusion Detection Systems (IDS). The main goal is to improve anomaly detection and predictive maintenance in smart manufacturing environments. The models' performance was assessed using key metrics such as precision, recall, accuracy, F1 score, and ROC AUC score, in addition to evaluating their training and prediction times. Results show that Bagging and Voting Classifiers achieved the highest accuracy and ROC AUC scores, making them highly effective for complex detection tasks. However, XGBoost and LightGBM demonstrated superior computational efficiency, making them suitable for real-time systems requiring fast prediction times. The findings indicate that ensemble learning techniques can significantly improve both the accuracy and speed of anomaly detection in IIoT systems, providing a robust framework for enhancing cybersecurity and operational efficiency in smart factories.