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<p>The rapid proliferation of Internet of Health Things (IoHT) devices has dramatically enlarged the attack surface of healthcare networks, where intrusion detection is challenged by high-dimensional, redundant features, evolving attack behaviors, and strict real-time constraints at edge or near-patient devices. This study aims to design a leakage-safe, latency-aware intrusion detection pipeline that simultaneously improves detection accuracy, robustness, and deployability in realistic IoHT environments. We propose a unified framework that combines a novel Transformer-Aware Wrapper Feature Selection (TAW-FS), a compact CNN–BiLSTM classifier, and a hybrid Whale–Grey Wolf Optimization (WOA–GWO) tuner: TAW-FS leverages transformer attention to rank features and iteratively retain a compact subset from the original 50 attributes, CNN–BiLSTM jointly captures local flow motifs and short-range temporal dependencies, and the hybrid tuner balances exploration and exploitation under a multi-objective, latency-aware fitness. Experiments on a publicly available IoHT intrusion benchmark comprising 188,694 flows (stratified 80/20 split) are conducted without test-set peeking, with all preprocessing, feature selection, and tuning confined to the training data. On the held-out test set (Attack as positive), the proposed method attains Accuracy 99.12%, Precision 99.14%, Recall 99.12%, F1-score 99.12%, MCC 98.50%, ROC–AUC 0.9916, and median inference latency of 0.88&#xa0;ms per sample, consistently outperforming strong baselines such as XGBoost, CatBoost, Temporal Convolutional Networks, and TE-1D under identical budgets; improvements are statistically significant under a two-sided Wilcoxon test (α = 0.05). These results demonstrate that the proposed TAW-FS + CNN–BiLSTM + WOA–GWO pipeline offers a novel, practically viable IoHT intrusion detector that is both highly accurate and well suited for real-time deployment in resource-constrained smart-healthcare networks.</p>

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IoHT attack detection using transformer-aware feature selection with CNN-BiLSTM optimized by hybrid WOA–GWO

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Tanvir Rahman Akash,
  • Abdul Azeem Mohammed,
  • Abdullah Al Farooq,
  • Ismoth Zerine,
  • Md Humayun Kabir,
  • Chala Wata

摘要

The rapid proliferation of Internet of Health Things (IoHT) devices has dramatically enlarged the attack surface of healthcare networks, where intrusion detection is challenged by high-dimensional, redundant features, evolving attack behaviors, and strict real-time constraints at edge or near-patient devices. This study aims to design a leakage-safe, latency-aware intrusion detection pipeline that simultaneously improves detection accuracy, robustness, and deployability in realistic IoHT environments. We propose a unified framework that combines a novel Transformer-Aware Wrapper Feature Selection (TAW-FS), a compact CNN–BiLSTM classifier, and a hybrid Whale–Grey Wolf Optimization (WOA–GWO) tuner: TAW-FS leverages transformer attention to rank features and iteratively retain a compact subset from the original 50 attributes, CNN–BiLSTM jointly captures local flow motifs and short-range temporal dependencies, and the hybrid tuner balances exploration and exploitation under a multi-objective, latency-aware fitness. Experiments on a publicly available IoHT intrusion benchmark comprising 188,694 flows (stratified 80/20 split) are conducted without test-set peeking, with all preprocessing, feature selection, and tuning confined to the training data. On the held-out test set (Attack as positive), the proposed method attains Accuracy 99.12%, Precision 99.14%, Recall 99.12%, F1-score 99.12%, MCC 98.50%, ROC–AUC 0.9916, and median inference latency of 0.88 ms per sample, consistently outperforming strong baselines such as XGBoost, CatBoost, Temporal Convolutional Networks, and TE-1D under identical budgets; improvements are statistically significant under a two-sided Wilcoxon test (α = 0.05). These results demonstrate that the proposed TAW-FS + CNN–BiLSTM + WOA–GWO pipeline offers a novel, practically viable IoHT intrusion detector that is both highly accurate and well suited for real-time deployment in resource-constrained smart-healthcare networks.