Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 11, No. 3, Nov 2022, pp. 233∼239
ISSN: 2089-4864, DOI: 10.11591/ijres.v11.i3.pp233-239 ❒ 233
Modeling arbiter-PUF in NodeMCU ESP8266 using
artificial neural network
Mohd Syafiq Mispan1, 3, 4
, Aiman Zakwan Jidin1, 3, 4
, Haslinah Mohd Nasir2, 3, 4
,
Noor Mohd Ariff Brahin1, 3, 4
, Illani Mohd Nawi5
1Micro and Nano Electronics (MiNE), Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka,
Durian Tunggal, Malaysia
2Advance Sensors and Embedded Controls System (ASECs), Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik,
Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
3Centre for Telecommunication Research and Innovation (CeTRI), Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik,
Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
4Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
5Jabatan Kejuruteraan Elektrik dan Elektronik, Fakulti Kejuruteraan, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
Article Info
Article history:
Received Jun 29, 2022
Revised Aug 7, 2022
Accepted Aug 26, 2022
Keywords:
Computational model
Hardware fingerprinting
Lightweight authentication
Machine learning
Physical unclonable function
ABSTRACT
A hardware fingerprinting primitive known as physical unclonable func-
tion (PUF) has a huge potential for secret-key cryptography and identifica-
tion/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 tech-
nology has a huge potential to be used for device identification and authentica-
tion 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.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mohd Syafiq Mispan
Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka
Hang Tuah Jaya street, 76100 Durian Tunggal, Melaka, Malaysia
Email: syafiq.mispan@utem.edu.my
1. INTRODUCTION
In recent years, the energy efficiency and security of the resource-constrained internet of things (IoT)
systems become a crucial issue and a major challenge. With the evolving security and privacy threats, software-
level security provides no guarantee to protect the system [1], [2]. Hence, hardware-level security technology
is required to enhance the system protection and ensure only the authenticated device or hardware can proceed
to the software or application launch. Nevertheless, providing another layer (i.e., hardware) of security level is
challenging especially for resource-constrained IoT systems.
Journal homepage: http://ijres.iaescore.com

234 ❒ ISSN: 2089-4864
The emergence of physical unclonable function (PUF) as hardware fingerprinting technology could
provide an intrinsic secret key or unique device-identifier by exploiting the inherent process variations during
integrated circuits (ICs) fabrication. The input-output of the PUF is known as challenge and response pairs
(CRPs). The unique and random corresponding responses are generated when challenges are applied to a
PUF. The PUF response is uniquely different from one challenge to the other challenges. Additionally, when
the same challenge is applied to two similar PUFs, each PUF generates a uniquely different response (i.e.,
device specific response). PUF design shows a few advantages such as low area and energy consumption, low
fabrication cost, device-specific response, and non-human key programming which reduce the potential threat
from untrusted parties in the interest to compromise the key [3].
Nevertheless, when considering the PUF application for resource-constrained device authentication,
one of the primary drawbacks is the establishment of the secret CRPs table in the verifier database [4], [5].
During the authentication, the server is securely communicating with the PUF by sending the challenge and
retrieving the PUF response as depicted in Figure 1. The authentication passes if the retrieved response is
matched with the stored response. To avoid replay attacks, the used CRPs must be discarded from the verifier
database and only unused CRPs are used for the next authentication process. Hence, the server must collect
a huge number of CRPs before the field application and store them secretly [6], [7]. For applications with
thousands (or could be millions) of PUF clients, this corresponds to an enormous amount of required secret
storage.
To overcome the limitation of the huge database in the verifier, the researchers explored the feasibility
of using a secret computational model of PUFs. In the early exploration, PUF-based authentication protocols
have been proposed in which the underlying PUFs need to be derived during the enrollment phase. The ex-
ample includes secure re-configurable PUF, time-bounded PUF, Slender PUF, noise bifurcation architecture,
and statistical delay-based PUF [8]-[12]. In a study, Kong et al. [13] proposed a PUF-based remote attestation
by binding the software-based attestation protocol to intrinsic device characteristics. Arithmetic and logic unit
(ALU) PUF is used as a PUF basic building based on the delay difference in two different ALUs caused by
the manufacturing variations. Moreover, the emulation-based approach is also proposed to overcome the huge
CRPs storage. The ALU PUF is emulated by extracting its gate-level delay and the delay additions. However,
during the read-out of the gate-level delays by a trusted party, the protected interface must be used. This pro-
tected interface must not be used by the user when the device is deployed in the field. Hence, it is suggested to
provide a protected interface that can be permanently disabled by, e.g., using fuses.
In a recent study, Yilmaz et al. [14] proposed a lightweight authentication for resource-constrained
IoT devices in which the PUF model is stored in the verifier’s database. The proposed authentication scheme
has been implemented on Zolertia Zoul devices based on server-client configuration. Elsewhere, Aghaie et al.
[15] proposed a fast and novel method to build the PUF model for delay-based PUFs implemented on FPGA
with only a few CRPs. The delay sensor is deployed as a readout circuit to characterize the delay of signals
traversing through the PUF components. The readout circuit is only present in the FPGA design at the trusted
party. The FPGA design shipped to the customer is not included with the readout circuit. Therefore, the design
released to the customers is secured as no direct readout mechanism attach to it. All the above studies show
that using a computational secret model of PUF for authentication protocol is feasible and it is getting attention
in the PUF research community.
Untrusted
Environment
Device A
PUF
Device A
PUF
Server
(Verifier)
C C
R’
R’
01101
01011
10101
01011
11010
10101
11011
00101
C R
Database A
01101
01011
10101
01011
11010
10101
11011
00101
C R
Database A
=?
C
R’
R
Figure 1. Authentication process using PUF technology
Int J Reconfigurable Embedded Syst, Vol. 11, No. 3, Nov 2022: 233–239

Int J Reconfigurable Embedded Syst ISSN: 2089-4864 ❒ 235
One of the potential resource-constrained applications that suit the PUF computational model is wire-
less sensor networks (WSN) [16], [17]. WSN is a technology used within an IoT system to sense and process
sensitive data [18]. Figure 2 illustrates the WSN which consists of a sink and sensor nodes. All the collected
data at the sensor nodes are forwarded to a sink node. Hence, before the exchange messages occur, the sensor
nodes must be authenticated by the sink to ensure their authenticity. WSN can be developed using NodeMCU
ESP8266 devices [19], [20]. Therefore, in this study, we design a computational model of PUF in NodeMCU
ESP8266 using an artificial neural network (ANN) to enable the lightweight authentication protocol develop-
ment in WSN. 32-bit arbiter-PUF is used as our case study for PUF computational model development.
Node 1 Node 2 Node n
Internet
Internet
User
Sink
Figure 2. Sink and sensor nodes concept in WSN
2. METHOD
Arbiter-PUF was proposed in [21], [22] which consists of k switching component and one arbiter
block as illustrated in Figure 3. The development of the arbiter-PUF computational model in NodeMCU
ESP8266 is divided into three major design steps. First, 32-bit arbiter-PUF architecture was constructed in
Cadence using 65-nm of CMOS technology node. Subsequently, the arbiter-PUF is simulated at 25 °C (room
temperature) and supply voltage of 1.2 V (nominal value). The inherent process variations are modeled using
Monte Carlo simulations within the Cadence environment. A total of 32000 CRPs have been collected for
building the computational model of arbiter-PUF using ANN.
Arbiter
(SRlatch)
A
B
Switching Component
Input
c1 = 0 c2 = 1 ck = 1
ck-1 = 0
∆t
Response
0 / 1
top0
bot0
top1
bot1
topk-1
botk-1
topk
botk
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0
Figure 3. 32-bit arbiter-PUF architecture (k=32)
The second design step is the characterization of collected CRPs using the ANN technique in a MAT-
LAB environment. The architecture of ANN used in our study is made-up of one input layer, one hidden
layer, and one output layer. 32 neurons are placed in the hidden layer and tan-sigmoid is used as the activation
function which is given as f(x) = 2
1+e−2x − 1. Whereas the linear activation function is used at the output
layer. Figure 4 depicts the ANN architecture as described above. Following [23], [24], the training algorithm
which has the optimum prediction accuracy, fast convergence time, and consistency is chosen, known as the
resilient back-propagation algorithm. 30,000 CRPs are chosen randomly to be used as a training data set and
the remaining 2,000 CRPs are used as the test data set. The weights and biases are extracted during the CRPs
characterization to be used in the third design step.
Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network (Mohd Syafiq Mispan)

236 ❒ ISSN: 2089-4864
c1
c2
ci
y1
y2
yj
ok
Ʃ
Ʃ
Ʃ
Ʃ
Input
layer
Hidden
layer, l=1
Output
layer, l=2
j,i
w(1)
w(1)
j,i
w(1)
w(2)
k,j
w(2)
k,j
θ1
θ1
θ2
θ2
θj
θj
θk
θk
j,i
w(l)
j,i
w(l)
i = node i of layer l-1
j = node j of layer l
l = layer no.
j,i
w(l)
i = node i of layer l-1
j = node j of layer l
l = layer no.
x
+1
-1
0
y
y=tansig(x)
x
+1
-1
0
y
y=tansig(x)
x
+1
-1
0
y
y=tansig(x)
x
+1
-1
0
y
y=tansig(x)
x
+1
-1
0
y
y=tansig(x)
x
+1
-1
0
y
y=tansig(x)
0
x
y
y=purelin(x)
0
x
y
y=purelin(x)
neuron
Figure 4. 3-layer of ANN archicture
For the third design step, an exact ANN architecture as simulated in MATLAB is designed in Node-
MCU ESP8266 and the extracted weights and biases are stored in the memory. Subsequently, the predictability
of the built ANN architecture is tested by using a similar 2,000 CRPs test data set. Its prediction result is
compared against simulated predictability in the MATLAB environment, and it is expected to be similar.
3. RESULTS AND DISCUSSION
3.1. Arbiter-PUF characterization
As mentioned in section 1, PUF generates the random responses by exploiting the inherent process
variations. The process variations are manifested in random delays in the IC. In our study, the random delay is
modeled using Monte Carlo simulation. As a result, each switching component in Figure 3 has its own unique
and random delay, which is also experienced by all the routing. When a rising pulse is applied at the input
and propagates to the final output, it is subjected to all these random delays. For ci=0, the paths for a rising
pulse is straight, while for ci=1 they are crossed. The accumulated delays at top32 and bot32 are evaluated
using SR-latch (i.e., an arbiter). Figure 5 represents the random delays of top and bottom paths generated by
two arbiter-PUFs in which both instances were applied with the same challenge. If top32 < bot32, a binary
response ‘1’ is generated. Otherwise, a binary response ‘0’ is generated. Based on delays in Figure 4, the
corresponding random response of ‘1’ and ‘0’ is generated respectively for PUF instances A and B. The results
indicate that the process variations are successfully modeled in arbiter-PUF using Monte Carlo simulation.
4.5 4.55 4.6 4.65 4.7 4.75
·10−9
0
0.2
0.4
0.6
0.8
1
1.2
∆t=29.6ps
Time [s]
Voltage
[V]
top-delay
bot-delay
4.55 4.6 4.65 4.7
·10−9
0
0.2
0.4
0.6
0.8
1
1.2
∆t=5.1ps
Time [s]
Voltage
[V]
top-delay
bot-delay
Figure 5. Rising pulses of two Arbiter-PUF instances (before the SR-latch) when applied with the same
challenge

3.2. Modeling accuracy
Based on the arbiter-PUF which was designed in section 3.1, 32,000 CRPs are collected for building
its computational model. An ANN technique is deployed for model-building as it can solve non-linear problems
[25]. Figure 6 depicts the prediction accuracy of the 32-bit arbiter-PUF computational model. When a training
set is small (i.e., 1,000 and below), the prediction accuracy is ≈90%. As the training set starts to increase larger
than 1,000 CRPs, the prediction accuracy improves and achieved ≈99.5% accuracy. The result shows that a
computational model of 32-bit arbiter-PUF is successfully built using the ANN technique. Subsequently, the
weights and biases are extracted for the computational model development in the NodeMCU ESP8266.
0 0.5 1 1.5 2 2.5 3
·104
0
20
40
60
80
100
CRPs
Prediction
Accuracy
[%]
Figure 6. Prediction accuracy of 32-bit Arbiter-PUF modeled using ANN technique
3.3. Estimation of memory usage
The usage of memory for 32-bit arbiter-PUF model implementation in the NodeMCU ESP8266 device
has been analyzed and summarized in Table 1. 7.1% of flash memory was occupied which consists of 5,200
bytes and 277,681 bytes, respectively for data and code. Whereas for SRAM, 50.3% was occupied which
represents about 32,208 bytes. Note that in this study, only a set of weights and biases assuming for one sensor
node has been stored in the memory. The NodeMCU ESP8266 device which has a database, DB to store the
weights and biases represents a sink or verifier used to authenticate the nodes. In practice, the DB should
consist of several sets of weights and biases for the identification and authentication process of sensor nodes in
the WSN application.
Table 1. Memory usage (in byte) of the 32-bit arbiter-PUF model
.text .data .bss Flash SRAM Total
277,681 5,200 27,008 282,881 32,208 309,889
4. CONCLUSION
PUF is a promising technology in the applications of identification/authentication and secret key gen-
eration. Although the PUF itself is a low-cost architecture, the deployment of the PUF in the authentication
application requires a huge database of CRPs. An increase in the area consumption could deter the prevalent
adoption of PUF, especially in resource-constrained systems. In this study, a computational model of Arbiter
PUF is developed by using the ANN technique. Our findings show that 32-bit arbiter-PUF can be modeled
with a very high accuracy of approximately 99.5%. Moreover, the computational model can be programmed
in the NodeMCU ESP8266 device. The programmed device can be used as a verifier or sink in the WSN ap-
plication for the sensor nodes authentication. Above all, this study gives insight into the design steps to build
the PUF model in any microcontroller by using available machine learning techniques to enable lightweight
authentication protocol development.

238 ❒ ISSN: 2089-4864
ACKNOWLEDGEMENT
The authors would like to thank the Ministry of Higher Education Malaysia and Universiti Teknikal
Malaysia Melaka for the financial funding to complete this project. Grant No. FRGS/1/2020/TK0/UTEM/02/56.
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BIOGRAPHIES OF AUTHORS
Mohd Syafiq Mispan received B.Eng Electrical (Electronics) and M.Eng Electrical (Com-
puter and Microelectronic System) from Universiti Teknologi Malaysia, Malaysia in 2007 and 2010
respectively. He had experienced working in semiconductor industries from 2007 until 2014 before
pursuing his Ph.D. degree. He obtained his Ph.D. degree in Electronics and Electrical Engineering
from University of Southampton, United Kingdom in 2018. He is currently a senior lecturer in Fakulti
Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka. His current
research interests include hardware security, CMOS reliability, VLSI design, and Electronic Systems
Design. He can be contacted at email: syafiq.mispan@utem.edu.my.
Aiman Zakwan Jidin is currently a Ph.D. candidate at Universiti Malaysia Perlis,
Malaysia. His research topic is focusing on optimizing memory testing algorithm efficiency for im-
proving fault coverage. Previously, he obtained his MEng in Electronic and Microelectronic System
from ESIEE Engineering Paris, France in 2011, before working as FPGA IP Core Design Engineer
at Altera Corporation Malaysia (now part of Intel). He is a full-time lecturer and researcher at Uni-
versiti Teknikal Malaysia Melaka (UTeM), in Electronic and Computer Engineering. His research
interests include DFT, VLSI, and FPGA system design. He can be contacted at email: aimanzak-
wan@utem.edu.my.
Haslinah Mohd Nasir received her Bachelor Degree in Electrical-Electronic Engineering
(2008) from Universiti Teknologi Malaysia (UTM), MSc (2016) and PhD (2019) in Electronic Engi-
neering from Universiti Teknikal Malaysia Melaka (UTeM). She had 5 years (2008-2013) experience
working in industry and currently a lecturer in UTeM. Her research interest includes microelectronics,
artificial intelligence and biomedical. She can be contacted at email: haslinah@utem.edu.my.
Noor Mohd Ariff Brahin received B.Eng Electrical (Electronics) from Universiti
Teknologi Malaysia, Malaysia in 2008. He had experienced working in semiconductor industries
from 2008 until 2013. He is currently a Teaching Engineer in Fakulti Teknologi Kejuruteraan Elek-
trik dan Elektronik, Universiti Teknikal Malaysia Melaka. His research interest include artificial
intelligence and VLSI design. He can be contacted at email: mohdariff@utem.edu.my.
Illani Mohd Nawi received the B.Eng. degree (Hons.) from the Universiti Teknologi
PETRONAS in 2002, and the M.Sc. degree in microelectronics systems design and the Ph.D. degree
in electrical and electronic engineering from the University of Southampton, Southampton, U.K., in
2004 and 2018, respectively. She is currently a lecturer in Department of Electrical and Electronic
Engineering, Faculty of Engineering, Universiti Teknologi PETRONAS. Her research interests ranges
from microelectronics systems design, robotics and automations, automotive-mems, bio-mems, and
reliability in IC design. She can be contacted at email: illani.nawi@utp.edu.my.

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