Intelligent Intrusion Detection in Industrial IoT Using Nature-Inspired Optimization and Machine Learning

Abstract

The rapid expansion of the Industrial Internet of Things (IIoT) has enabled the integration of smart sensors, industrial controllers, and cyber-physical systems across critical infrastructures such as manufacturing, energy, and automation. However, the heterogeneous and resource-constrained nature of IIoT networks introduces significant security challenges, particularly in detecting sophisticated cyber intrusions in real time. To address these challenges, this paper proposes an optimized intrusion detection framework for IIoT environments using machine learning combined with nature-inspired optimization techniques. The proposed methodology performs data preprocessing, including handling missing values and normalization, followed by feature selection using the Cyber Range Ant Optimization Algorithm to identify the most relevant industrial network attributes. The selected features are then utilized for model training and classification using the Cyber Ant Opto Boost Algorithm, which integrates XGBoost with Ant Colony Optimization for hyperparameter tuning.The model is implemented in a Python environment and evaluated using industrial network traffic datasets containing both normal and malicious IIoT activities. Experimental results demonstrate that the proposed approach improves detection accuracy while maintaining low computational overhead, making it suitable for resource-constrained industrial devices. The framework achieves superior performance compared to traditional machine learning and deep learning models in terms of accuracy, precision, recall, and F1-score.The proposed IIoT intrusion detection system offers a scalable, efficient, and real-time security solution for Industry 4.0 environments. By combining machine learning with nature-inspired optimization, the model enhances threat detection capabilities and ensures reliable protection of critical industrial infrastructures, making it well-suited for deployment in smart factories, industrial automation systems, and cyber-physical networks.