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A Review of Recent Developments on Secure Authentication Using RF Fingerprints Techniques

Year 2022, , 278 - 303, 31.12.2022

Hüseyin Parmaksız , Cihan Karakuzu

https://doi.org/10.35377/saucis...1084024
Cited By: 1

Abstract

The Internet of Things (IoT) concept is widely used today. As IoT becomes more widely adopted, the number of devices communicating wirelessly (using various communication standards) grows. Due to resource constraints, customized security measures are not possible on IoT devices. As a result, security is becoming increasingly important in IoT. It is proposed in this study to use the physical layer properties of wireless signals as an effective method of increasing IoT security. According to the literature, radio frequency (RF) fingerprinting (RFF) techniques are used as an additional layer of security for wireless devices. To prevent spoofing or spoofing attacks, unique fingerprints appear to be used to identify wireless devices for security purposes (due to manufacturing defects in the devices' analog components). To overcome the difficulties in RFF, different parts of the transmitted signals (transient/preamble/steady-state) are used.
This review provides an overview of the most recent RFF technique developments. It discusses various solution methods as well as the challenges that researchers face when developing effective RFFs. It takes a step towards the discovery of the wireless world in this context by drawing attention to the existence of software-defined radios (SDR) for signal capture. It also demonstrates how and what features can be extracted from captured RF signals from various wireless communication devices. All of these approaches' methodologies, classification algorithms, and feature classification are explained.
In addition, this study discusses how deep learning, neural networks, and machine learning algorithms, in addition to traditional classifiers, can be used. Furthermore, the review gives researchers easy access to sample datasets in this field.

Keywords

iot security deep learning RF fingerprinting software-defined radio

Supporting Institution

Bilecik Şeyh Edebali University Scientific Research Projects

Project Number

2021-01.BŞEÜ.01-01

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Year 2022, , 278 - 303, 31.12.2022

Hüseyin Parmaksız , Cihan Karakuzu

https://doi.org/10.35377/saucis...1084024
Cited By: 1

Abstract

Project Number

2021-01.BŞEÜ.01-01

References

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Details

Primary Language English
Subjects Computer Software, Software Engineering (Other)
Journal Section Articles
Authors

Hüseyin Parmaksız 0000-0001-8455-5625

Cihan Karakuzu 0000-0003-0569-098X

Project Number 2021-01.BŞEÜ.01-01
Publication Date December 31, 2022
Submission Date March 7, 2022
Acceptance Date October 11, 2022
Published in Issue Year 2022

Cite

IEEE H. Parmaksız and C. Karakuzu, “A Review of Recent Developments on Secure Authentication Using RF Fingerprints Techniques”, SAUCIS, vol. 5, no. 3, pp. 278–303, 2022, doi: 10.35377/saucis...1084024.

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