The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning‐based intrusion detection paradigm for IIoT with hybrid rule‐based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule‐based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well‐known network datasets, NSL‐KDD and UNSW‐NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL‐KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW‐NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

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.