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<p>The rapid growth of the Industrial Internet of Things (IIoT) has introduced complex security challenges requiring accurate, scalable, and privacy-preserving intrusion detection solutions. This paper presents a hybrid framework that combines Hunting Gradient Snake–Cuckoo Search (HGS–CS) feature optimization with a Transformer–LSTM detection model enhanced by a gated fusion mechanism under a federated learning setup. The proposed system comprises three main components: (1) a dual-phase HGS–CS optimizer that selects the most informative features by balancing global exploration and local refinement; (2) a gated Transformer–LSTM architecture that effectively captures both temporal dependencies and spatial correlations in IIoT traffic; and (3) an attention-weighted SwarmFed aggregation protocol that enables decentralized yet coordinated model training across multiple clients while preserving data privacy. Experimental evaluations on four benchmark IIoT datasets, CICAPT-IIoT, Edge-IIoTset, WUSTL-IIoT, and ToN-IoT, show that the proposed model achieves an average accuracy of 99.27%, precision of 99.25%, recall of 99.17%, F1-score of 99.21%, and AUC of 99.35%, surpassing recent state-of-the-art methods by 0.8–1.3% across all metrics. The model also exhibits low standard deviation (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\le \)</EquationSource> </InlineEquation>0.22%) across independent runs, confirming its robustness and stability. Overall, this study introduces a numerically validated, hybrid optimization and deep learning framework that delivers reliable, scalable, and privacy-aware intrusion detection for next-generation IIoT environments.</p>

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Intrusion detection in industrial internet of things network using feature optimization and hybrid deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

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  • Salam Fraihat,
  • Qussai Yaseen,
  • Yousef Sanjalawe,
  • Aymen Abu-Errub,
  • Sharif Naser Makhadmeh,
  • Mohammed Azmi Al-Betar

摘要

The rapid growth of the Industrial Internet of Things (IIoT) has introduced complex security challenges requiring accurate, scalable, and privacy-preserving intrusion detection solutions. This paper presents a hybrid framework that combines Hunting Gradient Snake–Cuckoo Search (HGS–CS) feature optimization with a Transformer–LSTM detection model enhanced by a gated fusion mechanism under a federated learning setup. The proposed system comprises three main components: (1) a dual-phase HGS–CS optimizer that selects the most informative features by balancing global exploration and local refinement; (2) a gated Transformer–LSTM architecture that effectively captures both temporal dependencies and spatial correlations in IIoT traffic; and (3) an attention-weighted SwarmFed aggregation protocol that enables decentralized yet coordinated model training across multiple clients while preserving data privacy. Experimental evaluations on four benchmark IIoT datasets, CICAPT-IIoT, Edge-IIoTset, WUSTL-IIoT, and ToN-IoT, show that the proposed model achieves an average accuracy of 99.27%, precision of 99.25%, recall of 99.17%, F1-score of 99.21%, and AUC of 99.35%, surpassing recent state-of-the-art methods by 0.8–1.3% across all metrics. The model also exhibits low standard deviation ( \(\le \) 0.22%) across independent runs, confirming its robustness and stability. Overall, this study introduces a numerically validated, hybrid optimization and deep learning framework that delivers reliable, scalable, and privacy-aware intrusion detection for next-generation IIoT environments.