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Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network

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)...

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Main Authors: Mispan, Mohd Syafiq, Jidin, Aiman Zakwan, Mohd Nasir, Haslinah, Brahin, Noor Mohd Ariff, Mohd Nawi, Illani
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26294/2/2022_MODELING%20ARBITER-PUF%20IN%20NODEMCU%20ESP8266%20USING%20ARTIFICIAL%20NEURAL%20NETWORK.PDF
http://eprints.utem.edu.my/id/eprint/26294/
https://ijres.iaescore.com/index.php/IJRES/article/view/20535
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spelling my.utem.eprints.262942023-07-25T15:01:56Z http://eprints.utem.edu.my/id/eprint/26294/ Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network Mispan, Mohd Syafiq Jidin, Aiman Zakwan Mohd Nasir, Haslinah Brahin, Noor Mohd Ariff Mohd Nawi, Illani 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. Institute of Advanced Engineering and Science 2022-11 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26294/2/2022_MODELING%20ARBITER-PUF%20IN%20NODEMCU%20ESP8266%20USING%20ARTIFICIAL%20NEURAL%20NETWORK.PDF 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 https://ijres.iaescore.com/index.php/IJRES/article/view/20535 10.11591/ijres.v11.i3.pp233-239
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description 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.
format Article
author Mispan, Mohd Syafiq
Jidin, Aiman Zakwan
Mohd Nasir, Haslinah
Brahin, Noor Mohd Ariff
Mohd Nawi, Illani
spellingShingle Mispan, Mohd Syafiq
Jidin, Aiman Zakwan
Mohd Nasir, Haslinah
Brahin, Noor Mohd Ariff
Mohd Nawi, Illani
Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
author_facet Mispan, Mohd Syafiq
Jidin, Aiman Zakwan
Mohd Nasir, Haslinah
Brahin, Noor Mohd Ariff
Mohd Nawi, Illani
author_sort Mispan, Mohd Syafiq
title Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
title_short Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
title_full Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
title_fullStr Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
title_full_unstemmed Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
title_sort modeling arbiter-puf in nodemcu esp8266 using artificial neural network
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26294/2/2022_MODELING%20ARBITER-PUF%20IN%20NODEMCU%20ESP8266%20USING%20ARTIFICIAL%20NEURAL%20NETWORK.PDF
http://eprints.utem.edu.my/id/eprint/26294/
https://ijres.iaescore.com/index.php/IJRES/article/view/20535
_version_ 1772816024688656384
score 13.211869

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