Towards a Hybrid Deep-Learning SDN-Based Intelligent Attack Detection System for the IoMT
Yahya Rbah, Mohammed Mahfoudi, Mohammed Fattah, Younes Balboul, Saïd Mazer, Moulhime El Bekkali, Kaouthar Chetioui, Benaissa Bernoussi · International Journal on Communications Antenna and Propagation (IRECAP) · 2024
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.