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IoT, Volume 3, Issue 2
June 2022 - 4 articles
Cover Story: Detecting abnormal traffic is one of the problematic areas for researchers in protecting network infrastructures from adversary activities. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not the only issue with current intrusion detection systems, as their efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed integrated evaluation metrics (IEMs) to determine the best performance of IDS models. The automated ranking and best selection method (RBSM) is performed using the proposed IEMs to select the best model for the ensemble classifier to detect highly accurate attacks using machine and deep learning approaches. View this paper
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IoT - ISSN 2624-831X