Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network

International Journal of Reconfigurable and Embedded Systems (IJRES) · Vol. 11 No. 3 (2022) · pp. 233–239

DOI
10.11591/ijres.v11.i3.pp233-239
ISSN
2722-2608, 2089-4864
Published
2022-11-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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 to be 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.

Keywords

Collaboration network of the authors

40 authors · 119 co-authorship links · 16 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universiti Teknikal Malaysia Melaka
  • Universiti Malaysia Perlis
  • Universitas Ahmad Dahlan
  • University of Technology Malaysia
  • Universiti Teknologi MARA
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Haslinah Mohd NasirAffiliation unknown6
Suraya ZainuddinUniversiti Teknikal Malaysia Melaka6
Nur Fatin Shazwani Nor RazmanUniversiti Teknikal Malaysia Melaka6
Hafez SarkawiUniversiti Teknikal Malaysia Melaka4
Razaidi HussinUniversiti Malaysia Perlis4
Lee Weng FookAffiliation unknown4
Idnin PasyaUniversiti Teknologi MARA3
Muhammad Adni KamaruddinUniversiti Teknikal Malaysia Melaka3
N. I. M. NorUniversiti Malaysia Perlis3
Idnin Pasya IbrahimAffiliation unknown3
Tole SutiknoUniversitas Ahmad Dahlan2
Auzani JidinUniversiti Teknikal Malaysia Melaka2
Wan Haszerila Wan HassanUniversiti Teknikal Malaysia Melaka2
Ahmad Hafizzudin MustafaUniversiti Teknikal Malaysia Melaka2
Mai Mariam Mohamed AminuddinAffiliation unknown2
Mohd Faizal ZulkifliAffiliation unknown2
Radi Husin RamleeAffiliation unknown2
Nik Rumzi Nik IdrisUniversity of Technology Malaysia1
Ahmad Fauzan KadminUniversiti Teknikal Malaysia Melaka1
Lim Siau LiUniversiti Teknikal Malaysia Melaka1
Siti Asma Che AzizUniversiti Teknikal Malaysia Melaka1
Norain RahimUniversiti Teknikal Malaysia Melaka1
Irna Nadira MahzanUniversiti Teknikal Malaysia Melaka1
A Shamsul Rahimi A SubkiUniversiti Teknikal Malaysia Melaka1
D. Abd HadiUniversiti Teknikal Malaysia Melaka1
Norfariza Ab WahabUniversiti Teknikal Malaysia Melaka1
Z. MadihaUniversiti Teknikal Malaysia Melaka1
Nurliyana Abd MutalibUniversiti Teknikal Malaysia Melaka1
Siti Halma JohariUniversiti Teknikal Malaysia Melaka1
Suziana AhmadUniversiti Teknikal Malaysia Melaka1
M. NuzaimahUniversiti Teknikal Malaysia Melaka1
A. Shamsul Rahimi A. SubkiAffiliation unknown1
Zahariah ManapAffiliation unknown1
Mohd Zaidi TumariAffiliation unknown1
Shahrizal SaatAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijres.iaescore.com/index.php/IJRES/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network</dc:title>
	<dc:creator>Mispan, Mohd Syafiq</dc:creator>
	<dc:creator>Jidin, Aiman Zakwan</dc:creator>
	<dc:creator>Nasir, Haslinah Mohd</dc:creator>
	<dc:creator>Brahin, Noor Mohd Ariff</dc:creator>
	<dc:creator>Nawi, Illani Mohd</dc:creator>
	<dc:subject xml:lang="en-US">Computational model; Hardware fingerprinting; Lightweight authentication; Machine learning; Physical unclonable function</dc:subject>
	<dc:description xml:lang="en-US">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 to be 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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universiti Teknikal Malaysia Melaka</dc:contributor>
	<dc:contributor xml:lang="en-US">Ministry of Higher Education Malaysia</dc:contributor>
	<dc:date>2022-11-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijres.iaescore.com/index.php/IJRES/article/view/20535</dc:identifier>
	<dc:identifier>10.11591/ijres.v11.i3.pp233-239</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Reconfigurable and Embedded Systems (IJRES); Vol 11, No 3: November 2022; 233-239</dc:source>
	<dc:source>2722-2608</dc:source>
	<dc:source>2089-4864</dc:source>
	<dc:source>10.11591/ijres.v11.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijres.iaescore.com/index.php/IJRES/article/view/20535/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Mohd Syafiq Mispan, Aiman Zakwan Jidin, Haslinah Mohd Nasir, Noor Mohd Ariff Brahin</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>