A Privacy-Preserving Deep Learning Framework for Real-Time Intrusion Detection in Internet of Medical Things (IOMT) Networks

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@article{1724360,
      author = {Olumhense Beauty Oboghelu, Omorogiuwa Osaremwinda},
      title = {A Privacy-Preserving Deep Learning Framework for Real-Time Intrusion Detection in Internet of Medical Things (IOMT) Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {3817-3828},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724360.pdf},
      abstract = {With the explosive growth of Internet of Medical Things (IoMT) devices in contemporary healthcare, the attack surface that is vulnerable to increasingly sophisticated cyber threats has grown to encompass a variety of devices and systems. The current intrusion detection systems (IDS) built with legacy machine learning algorithms have a high false-positive rate, feature engineering takes a lot of manpower, and the algorithms show limited generalisation when faced with zero-day attacks. To tackle these limitations, this research introduces a novel deep learning framework named CNN-HBO-GAN-FL which combines a Convolutional Neural Network (CNN) optimised using the Honey Badger Optimisation Algorithm (HBOA), a Generative Adversarial Network (GAN) for class-balanced data augmentation, and Federated Learning (FL) for privacy-preserving, distributed model training. This framework was then translated into Python using TensorFlow/Keras and tested on the CICIoMT2024 benchmark dataset. The experimental results demonstrate that the proposed framework attained 99.18% accuracy, 98.96% precision, 98.72% recall, 98.84% F1-score and 1.7% false-positive rate whereas traditional CNN, CNN-LSTM and CNN-PSO baselines achieved 96.99%, 97.55% and 97.74%, respectively. The model convergence time improved by around 35% compared to the baseline models when optimized using hyperparameter tuning based on HBOA, and the minority-class attack sensitivity was improved by 16% when the model was optimized using the augmentation based on the GAN. The federated learning architecture maintained the accuracy of centralised training by less than 0.4% variance, which has the potential to help ensure adherence to data-protection laws and regulations including HIPAA, the GDPR, and Nigeria's Data Protection Act 2023, but does not necessarily ensure compliance. The results demonstrate the potentiality of the proposed framework as a resource-efficient solution for preserving privacy in intrusion detection of future IoMT infrastructure.},
      keywords = {Internet of Medical Things (IoMT); intrusion detection system (IDS); convolutional neural network (CNN); Honey Badger optimization algorithm (HBOA); generative adversarial networks (GANs); federated learning; cybersecurity; deep learning},
      month = {October},
  }