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An advanced data analytics approach to a cognitive cyber-physical system for the identification and mitigation of cyber threats in the medical internet of things (MIoT)

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Abstract

The integration of connected devices into the Medical Internet of Things (MIoT) has improved healthcare delivery but also brings vulnerabilities to cyber threats to patient safety and the integrity of critical medical systems. Today’s security threats are no longer simple and traditional security mechanisms are no longer sufficient. The contribution of this research is an advanced data analysis approach based on a cognitive cyber-physical system to detect and prevent cyber-attacks in MIoT environments. In this paper, we introduce a framework that combines the whale optimization algorithm (WOA) with deep learning models such as gated recurrent units (GRU) and a dense neural network (DNN) for anomaly detection in cyber-attacks. Using a set of wearable health devices and medical imaging equipment, the system is trained to detect threats with unprecedented accuracy. The performance of the model is improved with Hyperparameter optimization using WOA. Experimental results show that the proposed GRU-DNN-WOA framework achieves a detection accuracy of 98.2%, precision of 97.1%, recall of 98.0%, and F1-score of 97.5% on the MedBIoT dataset. On the IoT-23 dataset, it achieves an accuracy of 96.8%, precision of 95.7%, recall of 96.6%, and F1-score of 96.1%, outperforming prior cybersecurity techniques. The results confirm the framework's robustness, scalability, and real-time applicability for securing the MIoT systems.

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Data availability statement

The datasets will be available on reasonable request from the corresponding author. The datasets will be available on reasonable request from the corresponding author.

Change history

  • 30 April 2025

    Funding project number has been updated.

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Funding

The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2025-1612).

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Authors and Affiliations

  1. School of Information Engineering, Hunan University of Science and Engineering, Yongzhou, 425199, Hunan, China

    Yayuan Tang

  2. Electronic Department, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India

    Suchi Mishra

  3. Department of Computer Science, College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia

    Noha Alduaiji

  4. University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal, Madhya Pradesh, India

    Piyush Kumar Shukla

  5. Department of Computer Science, Oakland University, Rochester, NY, USA

    Mohammad Yahya

  6. College of Education, Nanchang university of Technology, Nanchang, 330000, China

    Tao Pang

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Contributions

YT contributed to the conception, SM involved design and methodology, NA performed iterpretation of results, running codes and manuscript drafting; supervision, and investigation, PKS did supervision and investigation; MY contributed to methodology; and TP also contributed to methodology, result analysis.

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Correspondence to Tao Pang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare no conflict of interest. The authors declare no competing interests.

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During the preparation of this work, the author(s) have not used any tool, the author(s) reviewed and edited the content as needed and take (s) full responsibility for the content of the publication.

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Tang, Y., Mishra, S., Alduaiji, N. et al. An advanced data analytics approach to a cognitive cyber-physical system for the identification and mitigation of cyber threats in the medical internet of things (MIoT). J Supercomput 81, 623 (2025). https://doi.org/10.1007/s11227-025-07093-1

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