Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction

The increasing deployment of Internet of Things devices has introduced significant cyber security challenges, creating a need for robust intrusion detection systems. This research focuses on improving anomaly detection in industrial Internet of Things networks through feature reduction and selection. Experiments were performed to compare the effectiveness of Minimum Redundancy Maximum Relevance for feature selection with Principal Component Analysis for feature reduction. Six machine learning algorithms—Decision Trees, k-nearest neighbors, Gaussian Support Vector Machine, Neural Network, Support Vector Machines kernel, and Logistic Regression Kernel—were evaluated for both binary and multi-class classification using feature sets of 4, 12, 23, 50, and 79 features. The results reveal that Minimum Redundancy Maximum Relevance is superior to Principal Component Analysis in identifying crucial features. Notably, Minimum Redundancy Maximum Relevance achieves high accuracy with just 12 features, where the Decision Tree classifier reached an outstanding 99.9% accuracy in binary classification, and k-nearest neighbors achieved 99% accuracy in multi-class classification. The article emphasizes the critical role of feature engineering, with a specific focus on feature selection and reduction, and elaborates on applying MRMR and PCA algorithms to various feature sets. By comparing these methods, it showcases their influence on both model performance and complexity, leading to the development of more efficient and precise intrusion detection systems for Industrial IoT networks. What sets this study apart from previous ones is its novel demonstration of how these techniques significantly reduce training time and model complexity while maintaining or even improving performance, confirming the effectiveness of strategic feature utilization in strengthening Industrial IoT security by balancing accuracy, speed, and model size.