Title: Hybrid feature extraction and enhanced intrusion detection classification in industrial control networks

Authors: Hanlin Chen; Entie Qi; Jin Si; Hui Yan; Tong Zhou

Addresses: Changchun Institute of Technology, Changchun, 130012, China ' Changchun Institute of Technology, Changchun, 130012, China ' Changchun Institute of Technology, Changchun, 130012, China ' Suqian University, Jiangsu, 223800, China ' Changchun Xinheng Optoelectronic Information Technology Co., Ltd, Changchun, 130000, China

Abstract: In the rapidly developing industrial ecosystem, more and more malicious security attacks against industrial control come one after another. To address growing threats effectively, intrusion detection is essential in the multi-layer defence of communication networks. It helps prevent network attacks, policy violations, unauthorised access, and other security issues. It is very important to integrate this technology into the industrial control network. For anomaly identification, deep inspection of packets is required to extract appropriate features to identify attacks. Data usage and demand in industrial control networks are increasing daily; accurate anomaly detection with low data testing and training time is still challenging. This paper uses a hybrid feature extraction model consisting of a chi-square test, autoencoder, and principal component analysis. This paper presents a hybrid feature extraction-based intrusion detection model enhanced by a deep neural network. It is utilised to classify and identify various attack types within the non-intrusive learning packet dataset using the KDD dataset for industrial control networks, enabling an effective evaluation of intrusion detection performance. Experimental results demonstrate an average accuracy of 97.54%, a precision of 97.30%, and an F1-score of 94.01%. The model has better performance.

Keywords: industrial control network; deep neural network; DNN; feature extraction; chi-square test; autoencoder; principal component analysis; PCA; intrusion detection.

DOI: 10.1504/IJSNET.2025.147623

International Journal of Sensor Networks, 2025 Vol.48 No.3, pp.178 - 187

Received: 23 Dec 2024
Accepted: 08 Jan 2025
Published online: 24 Jul 2025 *

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