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

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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.

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Gupta, B. B., Gaurav, A., Attar, R. W., Arya, V., Alhomoud, A., & Chui, K. T. (2024). A Sustainable W-RLG Model for Attack Detection in Healthcare IoT Systems. Sustainability (Switzerland) , 16(8). https://doi.org/10.3390/su16083103

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