The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition

Özkan Yılmaz; Mehmet Akif Yazıcı

doi:10.35377/saucis...1138577

EN

The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition

Abstract

Physical layer authentication is an important technique for cybersecurity, especially in military scenarios. Device classification using radio frequency fingerprinting, which is based on recognizing device-unique characteristics of the transient waveform observed at the beginning of a transmission from a radio device, is a promising method in this context. In this study, the effect of the ambient temperature on the performance of radio device classification based on RF fingerprinting is investigated. The waveforms of the transient regions of the transmissions are recorded as images, and ResNet50 and InceptionV3 networks for image classification are used to determine the radio devices. The radio devices used in the study belong to the same brand, model, and production date, making the problem more difficult than classifying radio devices of different brands or models. Our results show that high levels of accuracy can be attained using convolutional neural network models such as ResNet50 and InceptionV3 when the test data and the training data are collected at the same temperature, whereas performance suffers when the test data and the training data belong to different temperature values. We provide the performance figures of a blended training model that uses training data taken at various temperature values. A comparison of the two networks is also provided.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence , Engineering

Journal Section

Research Article

Authors

Özkan Yılmaz
0000-0002-3793-5470
Türkiye

Mehmet Akif Yazıcı ^*
0000-0002-9965-2329
Türkiye

Publication Date

August 31, 2022

Submission Date

June 30, 2022

Acceptance Date

August 6, 2022

Published in Issue

Year 2022 Volume: 5 Number: 2

DOI

https://doi.org/10.35377/saucis...1138577

IZ

https://izlik.org/JA22HD44NP

Cite

RIS / Bibtex

APA

Yılmaz, Ö., & Yazıcı, M. A. (2022). The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition. Sakarya University Journal of Computer and Information Sciences, 5(2), 233-245. https://doi.org/10.35377/saucis...1138577

AMA

1.Yılmaz Ö, Yazıcı MA. The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition. SAUCIS. 2022;5(2):233-245. doi:10.35377/saucis.1138577

Chicago

Yılmaz, Özkan, and Mehmet Akif Yazıcı. 2022. “The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition”. Sakarya University Journal of Computer and Information Sciences 5 (2): 233-45. https://doi.org/10.35377/saucis. 1138577.

EndNote

Yılmaz Ö, Yazıcı MA (August 1, 2022) The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition. Sakarya University Journal of Computer and Information Sciences 5 2 233–245.

IEEE

[1]Ö. Yılmaz and M. A. Yazıcı, “The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition”, SAUCIS, vol. 5, no. 2, pp. 233–245, Aug. 2022, doi: 10.35377/saucis...1138577.

ISNAD

Yılmaz, Özkan - Yazıcı, Mehmet Akif. “The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition”. Sakarya University Journal of Computer and Information Sciences 5/2 (August 1, 2022): 233-245. https://doi.org/10.35377/saucis. 1138577.

JAMA

1.Yılmaz Ö, Yazıcı MA. The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition. SAUCIS. 2022;5:233–245.

MLA

Yılmaz, Özkan, and Mehmet Akif Yazıcı. “The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 2, Aug. 2022, pp. 233-45, doi:10.35377/saucis. 1138577.

Vancouver

1.Özkan Yılmaz, Mehmet Akif Yazıcı. The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition. SAUCIS. 2022 Aug. 1;5(2):233-45. doi:10.35377/saucis. 1138577

The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition

Öz

The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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Cited By

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