Bassam Mohammad Elzaghmouri1, Yosef Hasan Fayez Jbara2, Said Elaiwat3, Nisreen Innab4,*, Ahmed Abdelgader Fadol Osman5, Mohammed Awad Mohammed Ataelfadiel5, Farah H. Zawaideh6, Mouiad Fadeil Alawneh7, Asef Al-Khateeb8, Marwan Abu-Zanona8
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2299-2316, 2024, DOI:10.32604/cmc.2024.054836
- 18 November 2024
Abstract As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.… More >
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