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

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%.