REVIEW ARTICLE


Strength of Deep Learning-based Solutions to Secure Healthcare IoT: A Critical Review



Arul Treesa Mathew1, Prasanna Mani1, *
1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India


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Creative Commons License
© 2023 Mathew and Mani

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India; E-mail: prasanna.m@vit.ac.in


Abstract

Healthcare applications of IoT systems have gained huge popularity across the globe. From personal monitoring to expert clinical diagnosis, healthcare IoT systems have shown their importance to all possible extents. The ease of use and precise results add to the wide acceptance of such systems. However, this has also led to a magnificent increase in the number of attacks aimed at stealing or manipulating data as well as operations of HIoT-based healthcare assistance. Among the various modes of attacks, network-based attacks are found in the majority. In this work, we perform a critical review of these attacks, the existing countermeasures, and their limitations to understand and proclaim the importance of securing healthcare networks in the best possible manner. We also emphasize the necessity of deep learning-based smart solutions for securing healthcare systems, understanding the potential of deep learning in the security aspects being deployed in other genres of IoT applications. A comparative analysis of deep learning and machine learning-based security solutions is performed to examine their performances.

Keywords: Healthcare, Healthcare internet of things, Network attacks, Deep learning, IoT systems, Healthcare networks.