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A Sustainable W-RLG Model for Attack Detection in Healthcare IoT Systems

Author

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  • Brij B. Gupta

    (Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
    Department of Computer Science and Engineering, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
    Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune 412115, India
    Department of Electrical and Computer Engineering, Lebanese American University, Beirut 1102, Lebanon)

  • Akshat Gaurav

    (Computer Science and Engineering, Ronin Institute, Montclair, NJ 07043, USA)

  • Razaz Waheeb Attar

    (Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Varsha Arya

    (Department of Business Administration, Asia University, Taichung City 41354, Taiwan
    Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India)

  • Ahmed Alhomoud

    (Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia)

  • Kwok Tai Chui

    (Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University (HKMU), Hong Kong)

Abstract

The increasingly widespread use of IoT devices in healthcare systems has heightened the need for sustainable and efficient cybersecurity measures. In this paper, we introduce the W-RLG Model, a novel deep learning approach that combines Whale Optimization with Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) for attack detection in healthcare IoT systems. Leveraging the strengths of these algorithms, the W-RLG Model identifies potential cyber threats with remarkable accuracy, protecting the integrity and privacy of sensitive health data. This model’s precision, recall, and F1-score are unparalleled, being significantly better than those achieved using traditional machine learning methods, and its sustainable design addresses the growing concerns regarding computational resource efficiency, making it a pioneering solution for shielding digital health ecosystems from evolving cyber threats.

Suggested Citation

  • Brij B. Gupta & Akshat Gaurav & Razaz Waheeb Attar & Varsha Arya & Ahmed Alhomoud & Kwok Tai Chui, 2024. "A Sustainable W-RLG Model for Attack Detection in Healthcare IoT Systems," Sustainability, MDPI, vol. 16(8), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3103-:d:1372288
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    References listed on IDEAS

    as
    1. Ali M. Al Shahrani & Ali Rizwan & Manuel Sánchez-Chero & Carmen Elvira Rosas-Prado & Elmer Bagner Salazar & Nancy Awadallah Awad & Amandeep Kaur, 2022. "An Internet of Things (IoT)-Based Optimization to Enhance Security in Healthcare Applications," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, September.
    2. Shweta Kaushik & Charu Gandhi & Charu Gandhi, 2022. "Capability-Based Access Control With Trust for Effective Healthcare Systems," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(1), pages 1-28, January.
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