ARTICLE | doi:10.20944/preprints202302.0306.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Internet of Medical Things; physical unclonable functions; machine learning; security and privacy
Online: 17 February 2023 (08:40:29 CET)
The health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) technique is used to retrieve the health data back and for reducing security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.
ARTICLE | doi:10.20944/preprints202303.0499.v1
Subject: Medicine And Pharmacology, Other Keywords: Internet of Medical Things; Arbiter PUF; security and privacy; physical unclonable function; machine learning; authentication framework
Online: 29 March 2023 (03:25:19 CEST)
The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.