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- W4317038427 abstract "The Internet of Medical Things (IoMT) effectively tackles several shortcomings of conventional healthcare systems. It includes medical personnel shortages, patient care quality, insufficient medical supplies, and healthcare expenditures. There are several advantages of using IoMT technology for enhanced treatment efficiency and quality, thus improving patient health. However, the frequency and magnitude of cyberattacks on IoMT are increasing at a breakneck pace. Therefore, this article proposes a cyberattack detection method for IoMT-based networks using ensemble learning and fog-cloud architecture to address security issues. The ensemble technique employs a set of LSTM networks as individual learners at the first level and stacks a decision tree on top of them to classify attack and normal events. In addition, we present a framework for deploying the proposed IoMT-based approach as Infrastructure as a Service (IaaS) in the cloud and Software as a Service (SaaS) in the fog. The proposed method is evaluated on the ToN-IoT dataset, and the outcomes reveal that it surpasses the baseline approaches in terms of precision by 4%." @default.
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- W4317038427 date "2023-10-01" @default.
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- W4317038427 title "A Secure Ensemble Learning-Based Fog-Cloud Approach for Cyberattack Detection in IoMT" @default.
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- W4317038427 doi "https://doi.org/10.1109/tii.2022.3231424" @default.
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