Machine Learning and PUF based Authentication Framework for Internet of Medical Things
Pintu Kumar Sadhu, Anik Baul, Venkata P. Yanambaka, Ahmed Abdelgawad
Abstract
The advancement of technology enables the connection of humans with the digital system through the Internet of Things (IoT). Likewise, the internet of medical things (IoMT), is helping patients connect to doctors using medical equipment. To maintain the integrity of sensitive data and preserve privacy, IoMT requires robust authentication mechanisms. This paper proposes a lightweight authentication framework using physical unclonable function (PUF) and machine learning (ML). The framework needs 2.33ms as computation cost and 68 bytes as communication cost. Moreover, the ML shows 99.76% accuracy.
Topics & Concepts
Physical unclonable functionInternet of ThingsComputer scienceAuthentication (law)ByteThe InternetComputer securityComputer networkEmbedded systemCryptographyComputer hardwareWorld Wide WebPhysical Unclonable Functions (PUFs) and Hardware SecurityUser Authentication and Security SystemsNeuroscience and Neural Engineering