Open Access

ARTICLE

GSLDWOA: A Feature Selection Algorithm for Intrusion Detection Systems in IIoT

Wanwei Huang^1,*, Huicong Yu¹, Jiawei Ren², Kun Wang³, Yanbu Guo¹, Lifeng Jin⁴

1 College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450007, China
2 Information System Engineering College, PLA Cyberspace Force Information Engineering University, Zhengzhou, 450001, China
3 Intelligent Computing Research Department, Zhengzhou Xinda Institute of Advanced Technology, Zhengzhou, 450001, China
4 Network Security Research Department, Zhengzhou New Century Digital Technology Co., Ltd., Zhengzhou, 450007, China

* Corresponding Author: Wanwei Huang. Email:

(This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)

Computers, Materials & Continua 2026, 86(1), 1-24. https://doi.org/10.32604/cmc.2025.068493

Received 30 May 2025; Accepted 14 August 2025; Issue published 10 November 2025

Abstract

Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity. These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy. This paper proposes an industrial Internet of Things intrusion detection feature selection algorithm based on an improved whale optimization algorithm (GSLDWOA). The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to, such as local optimality, long detection time, and reduced accuracy. First, the initial population’s diversity is increased using the Gaussian Mutation mechanism. Then, Non-linear Shrinking Factor balances global exploration and local development, avoiding premature convergence. Lastly, Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in high-dimensional feature space. Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance. Compared to the traditional WOA algorithm, the detection rate and F1-score increased by 3.68% and 4.12%. On the WUSTL-IIoT-2021 dataset, accuracy, recall, and F1-score all exceed 99.9%.

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Keywords

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