Feature selection for enhanced IIoT security: a machine learning approach to intrusion detection

The Industrial Internet of Things (IIoT) has revolutionized smart manufacturing, automation, and critical infrastructure by enabling seamless device interconnectivity. However, this connectivity exposes IIoT systems to a wide spectrum of cyber threats, necessitating intelligent and adaptive intrusion detection mechanisms. In this study, we propose SMoFeLSTM—Sequential Modeling and Feature-Optimized Long Short-Term Memory—a deep learning-based intrusion detection framework tailored for multiclass attack classification in IIoT environments. The model incorporates meticulous preprocessing, including outlier elimination, label encoding, and temporal feature engineering, followed by MRMR-based feature selection and min–max normalization. SMoFeLSTM captures sequential dependencies using stacked LSTM layers trained with categorical cross-entropy loss and optimized via Adam. Experimental evaluations on the X-IIoTID dataset demonstrate superior performance, with an average accuracy of 96.97%, macro F1-score of 86.15%, and AUC of 98.42%, outperforming traditional and deep learning baselines. Class-wise results indicate robust detection of both frequent and rare attack categories. The proposed framework holds promise for scalable, explainable, and real-time IIoT security applications.