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Sanaa A Sharaf1, Sameer Nooh2

  • 1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

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Summary

This study introduces a novel framework for detecting adversarial attacks in the Internet of Medical Things (IoHT). The proposed model achieves high accuracy in identifying threats to protect sensitive healthcare data.

Keywords:
Adversarial attack detectionFeature selectionFederated learningMedical IoT networkRed-Tail hawk optimizer

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Area of Science:

  • Cybersecurity and Artificial Intelligence in Healthcare
  • Machine Learning and Deep Learning Applications

Background:

  • The Internet of Medical Things (IoHT) offers significant healthcare benefits but faces critical cybersecurity challenges, particularly adversarial attacks on neural networks.
  • Existing defense mechanisms struggle with real-world applicability, necessitating robust methods for detecting and mitigating AI vulnerabilities in critical healthcare systems.
  • Federated learning (FL) enables collaborative model training while protecting user privacy but remains susceptible to attacks from malicious participants.

Purpose of the Study:

  • To develop an advanced Adversarial Attack Detection Framework Using Federated Learning Empowered IoT Medical (AADF-FLEIoTM) model.
  • To enhance the security and reliability of AI methods in IoHT environments through effective adversarial attack detection.

Main Methods:

  • Implementation of a hybrid model integrating federated learning with advanced deep learning techniques.
  • Utilized min-max normalization for data preprocessing, the Marine Predator Algorithm (MPA) for feature selection, and a Convolutional Neural Network-Bidirectional Long Short-Term Memory with Self-Attention (SA-CNN-BiLSTM) for detection.
  • Optimized the SA-CNN-BiLSTM hyperparameters using the Red-Tail Hawk (RTH)-optimizer algorithm for superior classification performance.

Main Results:

  • The AADF-FLEIoTM model demonstrated superior performance in adversarial attack detection on an IoT healthcare security dataset.
  • Achieved a high accuracy of 98.24%, outperforming existing models in identifying and classifying threats within the IoHT environment.
  • The MPA and RTH-optimizer effectively enhanced feature selection and model hyperparameter tuning, respectively.

Conclusions:

  • The proposed AADF-FLEIoTM framework offers a robust and effective solution for detecting adversarial attacks in IoHT systems.
  • The integration of federated learning and advanced deep learning techniques significantly improves the security and reliability of medical IoT devices.
  • This study highlights the potential of hybrid AI models in safeguarding sensitive healthcare data against sophisticated cyber threats.