Muaadh A. Alsoufi1,*, Maheyzah Md Siraj1, Fuad A. Ghaleb2, Muna Al-Razgan3, Mahfoudh Saeed Al-Asaly3, Taha Alfakih3, Faisal Saeed2
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 823-845, 2024, DOI:10.32604/cmes.2024.052112
Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >
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