# EPS: Distinguishable IQ Data Representation for Domain-Adaptation Learning
of Device Fingerprints

Abdurrahman Elmaghbub  Oregon State University
<EMAIL_ADDRESS>Bechir Hamdaoui This work is supported in part by
NSF/Intel Award No. 2003273. Oregon State University
<EMAIL_ADDRESS>
# Domain-Agnostic Signal Representation for Hardware Fingerprinting-Based Zero
Trust Authentication

Abdurrahman Elmaghbub  Oregon State University
<EMAIL_ADDRESS>Bechir Hamdaoui This work is supported in part by
NSF/Intel Award No. 2003273. Oregon State University
<EMAIL_ADDRESS>
# Domain-Agnostic Hardware Fingerprints for Zero-Trust Wireless Device
Identity Identification

Abdurrahman Elmaghbub  Oregon State University
<EMAIL_ADDRESS>Bechir Hamdaoui This work is supported in part by
NSF/Intel Award No. 2003273. Oregon State University
<EMAIL_ADDRESS>
# Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-
Trust IoT Architecture

Abdurrahman Elmaghbub  Oregon State University
<EMAIL_ADDRESS>Bechir Hamdaoui This work is supported in part by
NSF/Intel Award No. 2003273. Oregon State University
<EMAIL_ADDRESS>
# Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-
Trust IoT Security

Abdurrahman Elmaghbub  Oregon State University
<EMAIL_ADDRESS>Bechir Hamdaoui This work is supported in part by
NSF/Intel Award No. 2003273. Oregon State University
<EMAIL_ADDRESS>
###### Abstract

Next-generation networks aim for comprehensive connectivity, interconnecting
humans, machines, devices, and systems seamlessly. This interconnectivity
raises concerns about privacy and security, given the potential network-wide
impact of a single compromise. To address this challenge, the Zero Trust (ZT)
paradigm emerges as a key method for safeguarding network integrity and data
confidentiality. This work introduces EPS-CNN, a novel deep-learning-based
wireless device identification framework designed to serve as the device
authentication layer within the ZT architecture, with a focus on resource-
constrained IoT devices. At the core of EPS-CNN, a Convolutional Neural
Network (CNN) is utilized to generate the device identity from a unique RF
signal representation, known as the Double-Sided Envelope Power Spectrum
(EPS), which effectively captures the device-specific hardware characteristics
while ignoring device-unrelated information. Experimental evaluations show
that the proposed framework achieves over 99%, 93%, and 95% of testing
accuracy when tested in same-domain (day, location, and channel), cross-day,
and cross-location scenarios, respectively. Our findings demonstrate the
superiority of the proposed framework in enhancing the accuracy, robustness,
and adaptability of deep learning-based methods, thus offering a pioneering
solution for enabling ZT IoT device identification.

##  I Introduction

In the ever-evolving landscape of intelligent and interconnected wireless
systems, the comprehensive connectivity, enabled by massive Internet of Things
(IoT) networks, and the surge in sophisticated cyber-attacks have highlighted
the pressing need for a revolutionary approach to network security.
Traditional approaches, built on the concept of a secure physical perimeter,
are falling short of the increasingly demanding security measures as the key
assumptions of these models no longer hold [1]. In response to this growing
concern, the Zero Trust (ZT) model has emerged as a transformative paradigm,
redefining the fundamental principles of network security [2]. Rooted in the
philosophy of “never trust, always verify,” the ZT model advocates for a
proactive and dynamic approach to security, where trust is no longer assumed,
but continuously verified throughout the entire network environment, and
resource access is granted solely based on device and user credentials,
irrespective of the user’s network location. The new paradigm offers special
attention to the identity authentication process as any misstep at this stage
can jeopardize the integrity of the entire system. The foundational
cornerstone within the ZT paradigm centers on the robust necessity for
unequivocal device identity. This imperative encompasses an array of
requisites including the hardware root of trust, passwordless authentication,
renewable credentials, and device registry [3]. In accordance with these
tenets, highly-secured devices possess a cryptographically-backed, unique, and
unforgeable “onboarding” identity that is inseparable from the hardware and
managed by an embedded security processor. They also enjoy a passwordless
authentication process leveraging digital certificates signed and verified
using private and public cryptographic keys. These certificates extend their
efficacy to provide renewable and operational credentials for continuously
secured operations. Lastly, it is crucial to have a device registry that
stores the core attributes of the devices to facilitate and audit the access
process. These requisites necessitate the incorporation of pivotal components
including cryptographic engines, security processors, and secure storage [3].
However, this integration poses challenges for the vast number of
microcontroller-based IoT devices, constrained by factors such as size, power,
or cost limitations. With countless interconnected devices forming integral
parts of expanding IoT ecosystems, the compromise of even seemingly innocent
devices can catalyze escalating threats, encompassing data pollution, lateral
movement, and denial-of-service attacks. Hence, safeguarding each individual
device within the network attains paramount significance.

To bridge this gap, we propose a novel hardware device fingerprinting-based
framework, named EPS-CNN, that builds a unique and unforgeable hardware-based
identity for IoT device authentication. Hardware device fingerprinting
technology serves as a powerful physical-layer security mechanism, enabling
device identification through the extraction of unique device fingerprints
embedded in the devices’ transmitted signals [4, 5]. These fingerprints emerge
due to inherent hardware manufacturing imperfections of various Radio
Frequency (RF) circuitry components yielding signal distortions [6] that
collectively shape distinctive device signatures.

Deep Learning (DL) stands out as a powerful computational paradigm for
embedding intelligence in IoT networks. It has been, recently, utilized for
device discovery, vulnerability analysis, anomaly detection, and trust-based
policy recommendations [7]. Although DL-based hardware device fingerprinting
methods have demonstrated promising results in terms of device identification
accuracy, several studies (e.g., [6, 8, 9, 10]) have revealed that many of
these methods do not perform well when the testing data is collected under a
domain that is different from that used during training. Here, the term domain
refers to the network setting/environment (e.g., channel condition, device
location, etc.) under which data is collected. This observation can be
attributed to the prevalent use of raw In-phase/Quadrature (IQ) data
representation as the input to the DL-based device identifiers [11]. However,
within the context of fingerprinting, this representation contains an
abundance of device-irrelevant information. Consequently, extracting
meaningful fingerprints from this raw IQ data becomes akin to finding a needle
in a haystack filled with numerous misleading needle-like objects.

To ensure a flawless authentication operation across various domains, the
domain-resilient property becomes an indispensable attribute for any device
identification framework aspiring to integrate seamlessly into the ZT
architecture. Accordingly, our proposed identification framework introduces a
novel RF signal representation, double-sided envelope’s power spectrum,
referred to as EPS for short, which significantly enhances the accuracy and
robustness of DL-based hardware device fingerprinting methods against domain
changes. Our EPS representation vividly captures the device’s hardware
impairments while suppressing device-irrelevant information. Specifically, it
closely mirrors the impaired behavior of a key RF hardware component, the
oscillator, whose impairment substantially contributes to the device’s unique
fingerprint and is proven resilient in the presence of variations in time,
channel conditions, and/or location. The adoption of the EPS representation is
pivotal in mitigating the impact of environmental changes on device
identification, ensuring the reliability and stability of our approach.

To generate this EPS representation, we extract the outer shape or envelope of
the IQ signal, thereby eliminating resultant amplitude offsets, and produce
the double-sided envelope’s power spectrum of the received burst, which serves
as an effective input to machine learning classifiers. Leveraging EPS, we then
propose the EPS-CNN device identification framework, which channels the output
of the EPS extractor engine into a standard Convolution Neural Network (CNN)
to create device-unique identities from received RF signals.

Through extensive evaluation on a testbed of 15 WiFi devices, we demonstrate
the effectiveness of our EPS-CNN device identification framework in real-world
scenarios. Notably, we show that our framework achieves an accuracy of over
99% in same-domain scenarios (i.e. training and testing are both done on same
day/location), and more importantly, consistently sustains an accuracy that
exceeds 95% for cross-location scenarios and 93% for cross-day scenarios.

The key contributions of this work can be summarized as:

  * •

We propose EPS, a novel IQ representation that significantly enhances the
performance of deep learning-based device identifiers. We demonstrate, through
experimentation, the distinguishability and reliability of EPS under varying
time, channel, and/or location domains.

  * •

We propose EPS-CNN, an EPS-based device fingerprinting framework that
substantially enhances the accuracy and robustness of device identification in
the presence of varying domains.

  * •

We assess the effectiveness of EPS-CNN in identifying WiFi devices and
showcase an exceptional cross-domain performance in real-world scenarios,
achieving an average testing accuracy of 93% and 95% respectively for the
cross-day and cross-location scenarios.

The rest of the paper is organized as follows. Sec. II studies the impact of
the oscillator’s carrier frequency inaccuracy and offset on the behavior of IQ
signals. Sec. III presents the proposed IQ data representation approach, EPS.
Sec. IV presents the proposed EPS-CNN identification framework along with its
extensive evaluation under different scenarios. Finally, Sec. V concludes the
paper.

##  II Understanding the Impact of Oscillator’s Frequency Inaccuracy on IQ
Signal Behavior

As highlighted earlier, recent studies [6, 9] have underscored a critical
drawback in the performance of DL models that solely rely on IQ samples,
revealing their struggles with maintaining consistency across diverse domains.
Consequently, there arises a pressing need for novel RF signal representations
that adeptly capture device-specific impairments. These representations are
essential to refine the feature selection process within DL-based
fingerprinting techniques and enhance their resilience to shifts in different
domains. In this work, we propose a device identification framework based on a
robust RF signal representation that accurately captures the impairments of
oscillators, which serves as the foundation for identity generation.

To be able to design such efficient representations, it is important to begin
by studying the impact of the carrier frequency inaccuracy caused by local
oscillators on the behavior of the received IQ signals.

###  II-A The Carrier Frequency Offset (CFO)

Local oscillators are essential hardware components in the RF transceiver
chain, primarily generating oscillating signals that serve as the foundation
for establishing RF communication. Therefore, the accuracy of the oscillator,
denoting the frequency offset from the specified target frequency, and its
stability, which refers to the frequency dispersion around its operational
value over time, are critical in RF applications, as they significantly impact
the overall system performance [12]. Carrier Frequency Offset (CFO) is a
hardware impairment that arises due to the mismatch between the receiver’s
local oscillating frequency and that of the sender. This mismatch often occurs
due to various factors, including Doppler shifts, oscillator inaccuracies, or
synchronization errors in communication systems [13].

###  II-B The Impact of Oscillator’s Frequency Inaccuracy

(a)

(b)

Figure 1: The time-domain IQ signal behavior across four different Pycom/IoT
devices.

To investigate the impact of the oscillating frequency inaccuracy on the IQ
signal behavior, we leveraged our testbed of 15 Pycom/IoT devices (shown in
Fig. LABEL:tx) to analyze the IQ signals collected from multiple different
(but identical in hardware) off-the-shelf devices and captured using a USRP
B210 receiver. This is done by having each of the 15 Pycom devices transmit
the same IEEE 802.11b WiFi packets after being powered on for 12 minutes to
ensure hardware stabilization. We want to emphasize here the importance of
waiting until the end of the warm-up period of the devices’ hardware before
starting the data collection process to ensure robust and consistent
measurements [14].

We show in Fig. 1 the behavior of both the I (in-phase, Fig 1(a)) and Q
(quadrature, Fig. 1(b)) components of the time-domain IQ signals captured from
four selected devices: Devices A, B, C, and D. We make a couple of key
observations from the figure. First, both the I and Q signals showed a
“sinusoidal” pattern in their envelopes, where the envelope of an oscillating
signal is the smooth boundary function that outlines the extremes of the
signal. More importantly, note that the number of “humps” of the envelope
varied among the devices. It is worth mentioning that the reported sinusoidal
behaviors of the IQ signal’s envelopes are observed across all of the 15
tested Pycom devices, but with each device exhibiting a slightly different
number of “humps”.

Two pivotal questions now emerge: (i) what underlies the sinusoidal pattern
seen in the IQ signal envelope, and (ii) what accounts for the varying count
of “humps” among different devices? We proceed to show that the main cause
behind such behavior is the CFO (carrier frequency offset) between the Pycom
device’s oscillating frequency and that of the receiver that exists due to the
inaccuracy of the device’s local oscillator. Specifically, we will next show
that the number of humps in the sinusoidal envelope depends on the CFO value.
This explains that the reason why different devices exhibit different numbers
of humps is that each device presents a different CFO value, which varies
across devices due to the device’s oscillator hardware imperfections incurred
during manufacturing.

(a)

(b)

(c)

(d)

Figure 2: Simulated time-domain I signal component.

###  II-C What Causes the Observed IQ Envelope Behavior?

We utilized MATLAB simulations to validate that the CFO is responsible for the
sinusoidal envelope behavior observed in the IQ signal of IoT devices. We
constructed a complete WLAN 802.11b system which we manipulated to vary the
CFO values between the transmitter and receiver. Various CFO impairments were
introduced, including scenarios with 0 Hz (ideal device), 50 Hz, 100 Hz, and
200 Hz. The CFO-impaired transmitted signal is then first passed through an
AWGN channel, and then down-converted and sampled by the receiver to generate
IQ data samples. Then, we extracted the real (I) components of the signals and
plotted them separately for CFO = 0 in Fig. 2(a), CFO = 50Hz in Fig. 2(b), CFO
= 100Hz in Fig. 2(c), and CFO = 200Hz in Fig. 2(d). The simulated results
clearly show the dependency between the CFO values and the number of observed
“humps” in the I signal’s Envelope, and that the CFO is what causes the
observed envelope shape. The same trends were observed for the Q signal
components as well, but we did not include them here to limit redundancy.

We want to mention that we also experimented with varying other hardware
impairments, including IQ imbalance, Phase Noise, and DC offset, but did not
observe any “sinusoidal” behavior of the envelopes. This confirms that other
transceiver hardware impairments, though do manifest themselves in other types
of distortions, do not yield the Envelope behavior we observed with the CFO
impairment.

##  III Distinguishable IoT Device Identification Through Novel RF Signal
Representation

In this section, we begin by presenting a novel RF signal representation
extracted from the oscillator’s envelope shape that substantially improves the
robustness of device fingerprinting to domain changes and variations. Next, we
assess the performance of the suggested feature design concerning its
capability to effectively serve two main purposes: (i) distinguish between
devices and (ii) withstand domain changes by maintaining high accuracy
performance under varying domains.

###  III-A The Proposed Double-Sided Envelope’s Power Spectrum (EPS)
Representation

To generate the proposed signal representation, we utilized a sequence of
operations that compose the EPS generator. The procedure involves creating an
analytic signal by initially processing the IQ values of the received frame,
denoted as $r(t)$, through an FIR Hilbert transform filter based on the Parks-
McClellan algorithm [15]. This filtered output is then scaled by $\sqrt{-1}$
and combined with the time-delayed original signal. Incorporating a delay is
crucial due to the inherent delay introduced by the FIR filter implementation
of the Hilbert transform, equating to half the filter’s length. Subsequently,
the signal’s envelope, denoted as $e(t)$, is derived by computing the absolute
value of the analytical signal. This envelope is characterized by a lower
frequency compared to the original signal. Consequently, we downsample the
signal by a factor of $15$ and then subject it to a lowpass filter to
effectively mitigate ringing and smoothen the envelope. Once the envelope is
extracted, we center its amplitude around zero before proceeding to generate
the corresponding normalized double-sided envelope’s power spectrum, i.e., the
EPS representation, utilizing a power spectrum estimator. The EPS
representations of various Pycom/IoT devices are visualized in Figure 3. This
representation possesses critical attributes in terms of both distinctiveness
and robustness, rendering it the suitable foundation for generating strong
device identities.

###  III-B EPS Distinguishability Across Different Devices

Figure 3: The EPS representation of 10 devices.

In the context of device identification, a signal representation that exhibits
distinctive device-specific characteristics is indispensable. The proposed EPS
representation possesses this property, as it captures the local oscillator’s
behavior, which is affected by the oscillator’s unique hardware impairments.
To validate this hypothesis, we conducted an experimental evaluation using a
testbed consisting of $15$ Pycom devices, running the IEEE802.11b protocol and
a USRP B210 receiver. Our results depicted in Fig. 3 reveal that the EPS
representation is indeed unique for each device, as evidenced by the
discernible differences, across the 10 studied devices, in the shape and
location of the main sideband and its harmonics. This will be further
validated through experimental results that are presented later in Sec. IV-B.

###  III-C EPS Robustness to Domain Changes

(a)

(b)

Figure 4: EPS representation resiliency across the channel and location
domains

Displaying distinctive features that are unique to each device is a crucial
aspect, but it alone isn’t enough for a representation to serve as a solid
foundation for generating device identities. When a representation of a device
exhibits random variations as the network’s context shifts, it becomes
incapable of providing a dependable fingerprint. Consequently, it cannot be
employed as an input for a reliable device identification system. Therefore,
having demonstrated the distinctiveness of the proposed representation through
our testbed, our focus now shifts to evaluating its resilience across three
different domains: time, channel, and location.

####  III-C1 Channel-Agnostic Fingerprinting

To thoroughly investigate how the wireless channel impacts the stability and
consistency of EPS, an extensive experiment was conducted within an indoor
environment. In this experimental arrangement, devices were consistently
positioned at a fixed distance of 1 meter from the receiver, in both wired and
wireless setups. Over three consecutive days, packets were captured and
analyzed. The objective of this investigation was to compare the EPS
representations of packets corresponding to each individual device across both
the wired and wireless channels over time, thereby discerning the influence of
channel variations over time. Fig. 4(a) presents the graphical representations
of the EPS representations obtained from four distinct devices under both
wired and wireless channel conditions. Notably, the figures demonstrate that
the EPS representation of each device remains unaltered regardless of the
underlying channel characteristics. This observation is consistent across all
15 devices, providing strong empirical support for the robustness and efficacy
of our proposed representation in mitigating sensitivity to channel variations
during device identity generation.

####  III-C2 Location-Agnostic Fingerprinting

Changing the location and distance between the transmitting devices and the
receiver after training can also lead to a drastic drop in performance. To
evaluate the robustness of the EPS representation to such distance and
location changes, we captured data at three different locations with the
devices being placed $1$m-away (Location A), $2$m-away (Location B), $3$m-away
(Location C), and randomly deployed within a radius of $3$m-away (Location D)
from the USRP receiver; this is shown in Fig. LABEL:loc_setup. The plots in
Fig. 4(b) manifest the stability of the EPS feature representation over the
four studied location scenarios as the signal representations at the four
locations completely overlap. Our findings confirm the stability of the EPS
representation in scenarios in which the location, distance, and time of the
training and testing sets are different, making the proposed EPS
representation a more reliable and robust input for wireless device
identification.

##  IV EPS-Based Fingerprinting Framework for Domain-Agnostic Device
Identification

Addressing the device identification challenges arising from domain shifts
remains a formidable task. This has hindered the practical adoption of deep
learning-aided fingerprinting-based device identification approaches in real-
world scenarios. In this section, we present our proposed device
identification framework, built upon the novel EPS feature representation, and
show its effectiveness in overcoming such domain-shift challenges by enhancing
the resiliency of device identification when faced with changes in the channel
condition, device location and/or time of data collection.

###  IV-A An Overview of the Proposed EPS-CNN Framework

Designing an architecture for integrating our EPS-CNN into a zero-trust IoT
network involves several components and considerations. At its highest level,
the operation flow can be described as follows. New IoT devices are enrolled
in the network through a secure enrollment/registration process, during which
each IoT device undergoes hardware device fingerprinting. The fingerprinting
of these devices, via the EPS generator, is initiated by taking the complex-
valued IQ representation of a received frame, $r(t)$, as an input (with a
dimension of 1x25170) and then processing the I and Q components separately.
For each frame, the EPS generator first extracts the envelope of the signal,
$e(t)$, and then generates the EPS representation of the two components:
EPS(I) and EPS(Q). Refer to Sec. III-A for details about the EPS
representation generation. These two EPS representations are then combined
into a tensor of size 2x4096, which plays a central role in training the CNN
model serving as the device identifier. Our device identifier encompasses a
structured arrangement of six convolutional blocks, which extract the
fingerprint from the EPS representation, three fully-connected layers with
LeakyReLUs in between, and a concluding Softmax layer, which makes
determinations regarding the device’s identity. Each convolutional block
includes 2D-Convolutional, BatchNormalization, LeakyReLU, and MaxPooling
layers. The device identifier’s primary function is to discern patterns within
the EPS input and make precise predictions about the corresponding device’s
identity. This completes the enrollment/registration phase.

Subsequently, when an IoT device attempts to access the network, it goes first
through a rogue device detection to confirm its legitimate affiliation with
the network. This is followed by identity verification, which employs the
hardware fingerprint EPS and leverages the device identifier to accurately
establish the device’s identity. For robust security, continuous
authentication is implemented on the packet level using the device’s received
RF signal during its interaction with the network. Any deviation from the
established EPS fingerprint triggers an alert for further investigation.

###  IV-B Performance Metrics

(a)

(b)

Figure 5: IoT Testbed consisting of 15 Pycom transmitting devices and a USRP
B210 receiving device

To assess the effectiveness of EPS-CNN, we considered two key performance
metrics: same-domain accuracy and cross-domain accuracy. Same-domain accuracy
measures the ability of the device identifier to identify devices accurately
when the testing data/packets are drawn from the same training domain. On the
other hand, cross-domain accuracy evaluates the models’ ability to generalize
across different domains, such as different locations, channels, or days. To
ensure robust results, we utilized the 5-fold cross-validation technique,
dividing each device’s data into five non-overlapping partitions of equal
size. Furthermore, we compared the performance of our proposed EPS-CNN with
the same CNN framework fed with a typical IQ representation as an input
(referred to as IQ-CNN).

###  IV-C Device Identification Results and Analysis

####  IV-C1 Robustness to Fixed-Location Changes

Figure 6: EPS-CNN performance: the testing accuracies under the location and
time scenarios

We begin by assessing the robustness of EPS-CNN to changes in device
locations, meaning that training and testing are done on data collected on
different (but fixed) locations. For this, we leveraged our 15-device testbed,
shown in Fig. LABEL:tx, to collect WiFi 802.11b data for three different
locations, Loc A, Loc B and Loc C, with devices being placed 1-meter, 2-meter
and 3-meter away from the USRP receiver, respectively (see Fig.
LABEL:loc_setup).

Our findings, depicted in Fig. 6(a)-(c), demonstrate remarkable identification
enhancements of EPS-CNN over IQ-CNN in cross-location scenarios, where
training and testing are done on different locations. For instance, when
training is done in Loc A but testing is done in Loc C (see Fig. 6(a)), the
average testing identification accuracy achieved under EPS-CNN exceeds 95%,
whereas that achieved under IQ-CNN is below 30%. Similar significant
enhancements are also seen when training is done in Loc B (Fig. 6(b)) or Loc C
(Fig. 6(b)) but testing is done in different locations. To the best of our
knowledge, this is the highest performance achieved by deep learning-based
device fingerprinting methods when training and testing are done on different
domains.

In addition to improving robustness to location changes remarkably, EPS-CNN
does achieve exceptional testing accuracy when testing and training are done
at the same location, whether Loc A, Loc B, or Loc C. Observe that the average
same-location testing accuracy achieved under EPS-CNN at Loc A, Loc B, and Loc
C are 100%, 99.6%, and 96.7%, respectively. It is worth mentioning that in the
case of same-location scenario, IQ-CNN too achieves high performances, as
shown in Fig. 6.

####  IV-C2 Robustness to Random-Location Changes

We now turn our attention to evaluating the effectiveness of EPS-CNN under
random placement of devices. For this experiment, during training (also
referred to as enrolment or registration), all devices transmit from a fixed
location, 1m away from the receiver, but during testing, the devices transmit
from random locations all within 3m from the USRP receiver. Fig. 6d shows that
EPS-CNN achieves high average cross-domain testing accuracies of 93% and 98%,
respectively, when trained on Loc A and tested under two random-location
deployments 1 and 2. In contrast, IQ-CNN’s performance deteriorates when
tested under random-location placements, whose achieved average testing
accuracies are only 40% and 58% under random-location deployments 1 and 2,
respectively.

####  IV-C3 Robustness to Time Changes

We now evaluate the resiliency of EPS-CNN under the cross-day scenario, where
training and testing are done on data collected on different days. For this,
we collected WiFi datasets over three consecutive days, where all devices were
placed 1 meter away from the receiver, and present the results of this
experiment in Fig. 6(e). The first observation we draw here is that the same-
day testing accuracies (both training data and testing data are collected on
the same day) under EPS-CNN and IQ-CNN were found to be 100% and 99%,
respectively. More interestingly, Fig. 6(e) shows that EPS-CNN (and IQ-CNN to
a lesser degree) maintains remarkable accuracy for the cross-day scenario,
where the average testing accuracies of EPS-CNN are 93% (compared to 88% for
IQ-CNN) and 92% (compared to 89% for IQ-CNN) when tested on Day 2 and Day 3
data, respectively. Similar significant enhancements were obtained when
trained on Days 2 and 3 and tested on the other days.

In recap, our findings shown in Fig. 6 demonstrate the superiority of our EPS-
CNN framework compared to the conventional IQ-CNN framework in overcoming
cross-location generalizability and adaptability.

###  IV-D Other Security Benefits

In addition to enhancing security, EPS-CNN preserves privacy by minimizing the
transmission of device credential information during network access
operations. Moreover, it eliminates the need for error-prone manual upkeep of
MAC Authentication Bypass/allow lists, as security policy rule recommendations
are automatically derived from EPS-CNN’s output.

The durability of our framework inherently depends on the aging rate of
hardware components, especially the quality of the crystal oscillator, and the
intra-distance between different fingerprints in the latent space.
Consequently, it varies between deployments. In our current evaluation, we
have concentrated on showcasing the resiliency of our framework against short-
term aging, a standard practice in the assessment of newly proposed device
fingerprinting techniques. This short-term analysis, conducted over a month,
forms a robust foundation for understanding initial performance. However, we
acknowledge the necessity to explore the model’s behavior under prolonged
aging circumstances, spanning months or even years, aligning with the typical
lifespan of IoT devices.

##  V Conclusion

In conclusion, this article addresses security challenges posed by limitations
in resource-constrained microcontroller-based devices, hindering their
integration into the ZT security paradigm due to insufficient capabilities for
robust identity-based access authentication. We present EPS-CNN, an innovative
wireless device identification solution tailored for ZT architecture,
emphasizing resource-constrained IoT devices. At its core is the unique EPS
representation, overcoming domain challenges and establishing a robust
foundation for device identity. Rigorous empirical validation demonstrates the
practical viability of our approach, contributing to the advancement of secure
IoT networks in an era marked by cyber threats and sophisticated attacks.

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Abdurrahman Elmaghbub received the B.S. degree with summa cum laude, and MS in
Electrical and Computer Engineering from Oregon State University in 2019, and
2021, respectively, and is currently pursuing his Ph.D. degree in the School
of Electrical Engineering and Computer Science at Oregon State University. His
research interests are in the area of wireless communication and networking
with a current focus on applying deep learning to wireless device
classification.
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Bechir Hamdaoui is a Professor in the School of Electrical Engineering and
Computer Science at Oregon State University. He received M.S. degrees in both
ECE (2002) and CS (2004), and the Ph.D. degree in ECE (2005) all from the
University of Wisconsin-Madison. His general interests are in theoretical and
experimental research that enhances the cybersecurity and resiliency of future
intelligent networked systems. He is the Founding Director of the NetSTAR
Laboratory at Oregon State University. Dr. Hamdaoui and his team have won
several awards, including the ISSIP 2020 Distinguished Recognition Award, the
2009 NSF CAREER Award, the ICC 2017 Best Paper Award, and the 2016 EECS
Outstanding Research Award. He serves/served as an Associate Editor for
several IEEE journals and magazines and chaired & organized many IEEE/ACM
conference symposia & workshop programs. He served as a Distinguished Lecturer
for the IEEE Communication Society in 2016 and 2017 and served as the Chair &
Co-chair of the IEEE Communications Society’s Wireless Technical Committee
(WTC) from January 2019 until December 2022.
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