Industrial 4.0 revolution has led to a deeper integration of industrialization and digitalization, resulting in industrial control systems (ICS) characterized by nonlinear and high-dimensional data. These complexities render traditional intrusion detection methods ineffective. In this study, we propose an intrusion detection model for ICS based on the coronavirus herd immunity optimizer (CHIO). The model leverages Fisher-Score and kernel principal component analysis (KPCA) for feature extraction, effectively reducing the complexity of the data. To enhance the search performance of the CHIO, adaptive mechanisms and differential evolution strategies are incorporated. The improved algorithm is then applied to a support vector machine (SVM) for parameter optimization. The performance of the model is validated using the natural gas pipeline dataset from the University of Mississippi. Experimental results demonstrate that the proposed model offers significant improvements in both detection accuracy and speed compared to traditional methods, achieving a detection rate of 97.1%.