An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

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Abstract

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day. Therefore, it is essential to make medical services connected to internet, available in every remote location during these situations. Also, the security issues in the Internet of Medical Things (IoMT) used in these service, make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures. Hence, services in the healthcare ecosystem need rapid, uninterrupted, and secure facilities. The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas. This research aims to develop an intelligent Software Defined Networks (SDNs) enabled secure framework for IoT healthcare ecosystem. We propose a hybrid of machine learning and deep learning techniques (DNN + SVM) to identify network intrusions in the sensor-based healthcare data. In addition, this system can efficiently monitor connected devices and suspicious behaviours. Finally, we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios. the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

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