Kavita Sharma (editor) - Internet of Healthcare Things: Machine Learning for Security and Privacy (Machine Learning in Biomedical Science and Healthcare Informatics)

Scrivener Publishing

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Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ()

Phillip Carmical ()

Edited by

Kavita Sharma,

Yogita Gigras,

Vishnu Sharma,

D. Jude Hemanth

and

Ramesh Chandra Poonia

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-79176-8

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

In recent years, the use of the Internet of Things (IoT) has been on the rise worldwide, bringing with it new challenges and possibilities along with new cybersecurity risks in the area of IoT-enabled healthcare. These new challenges involve smart connectivity, high security, and confidentiality, generating big data, reducing total data latency between machine-to-machine interfaces, and reducing bandwidth, complexity, and power consumption. In the healthcare sector, the IoT has made it possible for devices to monitor patients health remotely, unleashing the capability to ensure their health and allowing physicians to deliver unmatched care. These IoT devices are more efficient for identifying disease in less time with more precision and have an absence of network segmentation, inadequate access control of legacy systems, and an increased susceptible surface area that cyber attackers exploit. Therefore, even though the IoT has significantly impacted healthcare costs and improving treatment results, IoT-enabled healthcare devices greatest common threats are data safety and confidentiality. Since these devices communicate and obtain data in real-time, cybercriminals can break into the system and steal the Personal Health Information (PHI) of both patients and their doctors. Even so, the IoT is certainly improving the healthcare sector by redefining the scope of the devices.

Another significant threat is the combination of numerous network devices, which creates difficulties in implementing the IoT in the healthcare sector. The enormous amount of data produced by these devices can also impede the ability of doctors to identify diseases. So, this book addresses these issues and provides solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithm, etc. Machine learning-enabled IoT devices deliver the information concealed in data for fast, computerized responses and enhanced decision-making. This information might be used to plan for upcoming patterns, distinguish anomalies, and expand intelligence by audio, image and video analyses. The IoT permits devices to send information to isolate blockchain networks to generate tamper-resistant accounts of collective transactions. Blockchain empowers business associates to access and share IoT data but without the necessity of central management and control.

As the world is entering the fourth industrial revolution-otherwise known as Industry 4.0-the combination of the IoT with other technologies, such as cybersecurity, big data, cloud computing and blockchain, is fundamentally changing the healthcare industry. The development of these fields is critical in healthcare because it improves the quality and efficiency of treatments and improves the patients health.

This books main objective is to motivate the reader to use telemedicine facilities to monitor patients in remote areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses the significant threats: the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through specific protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing vast amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book.

The 12 chapters of the book are organized into three sections. The first section consists of three chapters on data security and privacy concerns in the IoHT. The second section contains five chapters describing the application of machine learning, blockchain, and fog computing in the IoHT. The third section discusses the latest case studies in the healthcare sector.

• discusses the changes and standards required for the healthcare sector, covering privacy and security concerns, requirements, existing solutions, future challenges, and privacy-preserving methods. This chapter focus to enhance the knowledge of monitoring for adverse medical events and leading to a rise in the quality of treatment for diseases.