Towards a Hybrid Deep-Learning SDN-Based Intelligent Attack Detection System for the IoMT

Yahya Rbah; Mohammed Mahfoudi; Mohammed Fattah; Younes Balboul; Said Mazer; Moulhime Elbekkali; Kaouthar Chetioui; Benaissa Bernoussi

doi:10.15866/irecap.v14i1.24493

Towards a Hybrid Deep-Learning SDN-Based Intelligent Attack Detection System for the IoMT

Yahya Rbah^(1*), Mohammed Mahfoudi⁽²⁾, Mohammed Fattah⁽³⁾, Younes Balboul⁽⁴⁾, Said Mazer⁽⁵⁾, Moulhime Elbekkali⁽⁶⁾, Kaouthar Chetioui⁽⁷⁾, Benaissa Bernoussi⁽⁸⁾

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The DOI number for this article will be assigned as soon as the final version of the IRECAP Vol 14, No 1 (2024) issue will be available

Abstract

The Internet of Medical Things (IoMT) has emerged from integrating medical devices into the Internet of Things (IoT), transforming various healthcare applications, including real-time monitoring and remote patient care. This paper proposes a novel hybrid deep learning framework for intrusion detection within the IoMT environment. The framework leverages the strengths of Long Short-Term Memory (LSTM) networks for sequential data processing and an attention layer to capture both long- and short-term dependencies within the data. This approach is embedded within a Software-Defined Network (SDN) architecture to enhance the efficiency of intrusion detection. The proposed method achieves high accuracy (99.99%) and rapid processing times (<1.85 seconds) on the "IoT-Healthcare security" dataset, demonstrating its effectiveness against prevalent threats. Comparative analysis with benchmark models showcases superior performance in terms of both accuracy and computational complexity.
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Keywords

Internet of Medical Things; Cyber-Security; Intrusion Detection System; Deep Learning; Software Defined Network

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