An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses security concerns affecting the IIoT system, and due to device vulnerabilities, IIoT has encountered several attacks. Therefore, security features, such as encryption, authorization control, and verification, have been applied in IIoT networks to secure network nodes and devices. However, the requisite machine learning models require some time to detect assaults because of the diverse IIoT network traffic properties. Therefore, this study proposes ensemble models enabled with a feature selection classifier for Intrusion Detection in the IIoT network. The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_IoT datasets. The performance of these models is appraised based on accuracy, recall, precision, F1-score, and confusion matrix. The results indicate that the XGBoost ensemble showed superior performance with the highest accuracy over other models across the datasets in detecting and classifying IIoT attacks.

Automated summary

The Industrial Internet of Things (IIoT) connects heterogeneous and self-organized smart sensors and devices and is widely used to enhance user experience. However, security concerns arise due to the heterogeneity in node sources and the vulnerability of devices, leading to attacks on the IIoT system. To address this, security features such as encryption and authorization control have been applied. This study proposes the use of ensemble models with feature selection for Intrusion Detection in the IIoT network, with the XGBoost ensemble model showing superior performance in detecting and classifying IIoT attacks.