| Autor: |
Rbah, Yahya, Mahfoudi, Mohammed, Fattah, Mohammed, Balboul, Younes, Mazer, Said, El Bekkali, Moulhime, Chetioui, Kaouthar, Bernoussi, Benaissa |
| Předmět: |
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| Zdroj: |
International Journal on Communications Antenna & Propagation; Feb2024, Vol. 14 Issue 1, p34-41, 8p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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