Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network

Mispan, Mohd Syafiq and Jidin, Aiman Zakwan and Mohd Nasir, Haslinah and Brahin, Noor Mohd Ariff and Mohd Nawi, Illani (2022) Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network. International Journal of Reconfigurable and Embedded Systems (IJRES), 11 (3). pp. 233-239. ISSN 2089-4864

Text 2022_MODELING ARBITER-PUF IN NODEMCU ESP8266 USING ARTIFICIAL NEURAL NETWORK.PDF Download (992kB)

Abstract

A hardware fingerprinting primitive known as physical unclonable function (PUF) has a huge potential for secret-key cryptography and identification/authentication applications. The hardware fingerprint is manifested by the random and unique binary strings extracted from the integrated circuit (IC) which exist due to inherent process variations during its fabrication. PUF technology has a huge potential to be used for device identification and authentication in resource-constrained internet of things (IoT) applications such as wireless sensor networks (WSN). A secret computational model of PUF is suggested tobe stored in the verifier’s database as an alternative to challenge and response pairs (CRPs) to reduce area consumption. Therefore, in this paper, the design steps to build a PUF model in NodeMCU ESP8266 using an artificial neural network (ANN) are presented. Arbiter-PUF is used in our study and NodeMCU ESP8266 is chosen because it is suitable to be used as a sensor node or sink in WSN applications. ANN with a resilient back-propagation training algorithm is used as it can model the non-linearity with high accuracy. The results show that ANN can model the arbiter-PUF with approximately 99.5% prediction accuracy and the PUF model only consumes 309,889 bytes of memory space.

Item Type:	Article
Uncontrolled Keywords:	Computational model, Hardware fingerprinting, Lightweight authentication, Machine learning, Physical unclonable function
Divisions:	Faculty of Electrical and Electronic Engineering Technology > Department of Electronic and Computer Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Mar 2023 11:50
Last Modified:	25 Jul 2023 15:01
URI:	http://eprints.utem.edu.my/id/eprint/26294
Statistic Details:	View Download Statistic

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