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<p>The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has increased the need for intrusion detection systems that remain reliable under severe class imbalance, heterogeneous protocol behavior, and rare but high-impact attacks. Existing IIoT detectors often achieve high aggregate accuracy but remain sensitive to manual hyperparameter tuning and underperform on minority attack classes. This study proposes an Attention-Guided Quantum-Inspired Extreme Learning Machine with Grey Wolf Optimization for rare-attack detection in IIoT networks. The framework represents each Edge-IIoT flow as a fixed-order 48-feature vector and uses a multi-head self-attention encoder with learned feature-index embedding to model cross-feature structural dependencies. A Quantum-Inspired Extreme Learning Machine provides closed-form classification through quantum-rotation-based hidden-layer initialization, while Grey Wolf Optimization selects seven key hyperparameters. Focal Loss with inverse-frequency class weighting is used to improve learning from underrepresented classes. The model was evaluated on the Edge-IIoT dataset containing 1,909,671 records, 15 traffic classes, and 48 features under binary and multi-class settings. In binary Normal-versus-Attack classification, the proposed model achieved 99.54% accuracy, 0.9942 F1-score, and 0.9997 AUC. In the 15-class setting, it achieved 98.94% accuracy, 0.9871 macro-precision, 0.9848 macro-recall, 0.9859 macro-F1, and 0.0079 FPR. Rare-class gains were most evident for MITM, where F1 increased by 6.57% points over the strongest baseline, and for Ransomware, where F1 increased by 1.34% points. Across five matched runs, the proposed model obtained the highest mean macro-F1 with the lowest variability. Friedman testing confirmed significant rank differences among models (χ² = 50.00, df = 10, <i>p</i> = 2.67 × 10⁻⁷), and Holm-corrected paired tests showed statistically reliable improvements over all baselines. The results indicate that combining attention-based feature modeling, closed-form QELM classification, metaheuristic tuning, and imbalance-aware learning improves rare-attack detection while maintaining efficient inference.</p>

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Attention-guided quantum-inspired extreme learning machine with grey wolf optimization for rare-attack detection in IIoT networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Md Shahiduzzaman,
  • Md Sultanul Arefin Sourav,
  • Maria Kabtia,
  • Ramisa Samin Shammah,
  • Md Azharul Islam,
  • Partha Chakraborty,
  • MD Mizanur Rahman,
  • Mazharul Islam,
  • Rahat Bin Aziz Tonoy,
  • Mohammed Adnan

摘要

The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has increased the need for intrusion detection systems that remain reliable under severe class imbalance, heterogeneous protocol behavior, and rare but high-impact attacks. Existing IIoT detectors often achieve high aggregate accuracy but remain sensitive to manual hyperparameter tuning and underperform on minority attack classes. This study proposes an Attention-Guided Quantum-Inspired Extreme Learning Machine with Grey Wolf Optimization for rare-attack detection in IIoT networks. The framework represents each Edge-IIoT flow as a fixed-order 48-feature vector and uses a multi-head self-attention encoder with learned feature-index embedding to model cross-feature structural dependencies. A Quantum-Inspired Extreme Learning Machine provides closed-form classification through quantum-rotation-based hidden-layer initialization, while Grey Wolf Optimization selects seven key hyperparameters. Focal Loss with inverse-frequency class weighting is used to improve learning from underrepresented classes. The model was evaluated on the Edge-IIoT dataset containing 1,909,671 records, 15 traffic classes, and 48 features under binary and multi-class settings. In binary Normal-versus-Attack classification, the proposed model achieved 99.54% accuracy, 0.9942 F1-score, and 0.9997 AUC. In the 15-class setting, it achieved 98.94% accuracy, 0.9871 macro-precision, 0.9848 macro-recall, 0.9859 macro-F1, and 0.0079 FPR. Rare-class gains were most evident for MITM, where F1 increased by 6.57% points over the strongest baseline, and for Ransomware, where F1 increased by 1.34% points. Across five matched runs, the proposed model obtained the highest mean macro-F1 with the lowest variability. Friedman testing confirmed significant rank differences among models (χ² = 50.00, df = 10, p = 2.67 × 10⁻⁷), and Holm-corrected paired tests showed statistically reliable improvements over all baselines. The results indicate that combining attention-based feature modeling, closed-form QELM classification, metaheuristic tuning, and imbalance-aware learning improves rare-attack detection while maintaining efficient inference.