Guest Editors
Dr. Praveen Malik, Lovely Professional University, India.
Dr. Rajesh Singh, Lovely Professional University, India.
Dr. Anita Gehlot, Lovely Professional University, India.
Summary
When
applied to the healthcare business, the Internet of Medical Things (IoMT) is an
extension of the Internet of Things (IoT) concept. Services can be implanted
over the human body in the IoMT framework, which generates sensitive and
private data that must be processed at several places for diagnostic,
analytical, therapy, and learning reasons. This helps the healthcare business
in a variety of ways. On the other side, it exacerbates a number of security
issues and raises worries about privacy. The goal is to secure the IoMT
infrastructure from end to end, including devices (such as wearables, storage,
and gadgets), communication routes, and data.In addition to security, we also
have to consider the sensitiveness of the data and provide privacy-related
solutions.
To
protect the IoMT system, a variety of security solutions are used, including
blockchain, deep learning methods, access control mechanisms, intrusion
detection and prevention systems, cryptographic algorithms, keyless techniques,
smart card or token-based security, Artificial Intelligence, and machine
learning approaches. The IoMT industry's rapid growth has resulted in new
assaults and weaknesses for attackers and malevolent users Limited research
efforts that could address the security concerns have been found, and a
suggestion for improving IoMT security has been proposed. Thus, it is essential
to investigate new security solutions and attack detection tools to protect the
developed system.
The
purpose of this special area is to compile high-quality studies and continue
research on the above-mentioned issue. The study project addresses the IoMT
industry's current taxonomy and security concerns. Researchers, developers, and
practitioners from academia and industry are invited to submit novel research
articles, both theoretical and experimental.
Keywords
Artificial Intelligence, Blockchain, IoMT networks, Machine Learning
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