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Leveraging Deep Learning for IoT Transceiver Identification
With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then propo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453519/ https://www.ncbi.nlm.nih.gov/pubmed/37628220 http://dx.doi.org/10.3390/e25081191 |
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author | Gao, Jiayao Fan, Hongfei Zhao, Yumei Shi, Yang |
author_facet | Gao, Jiayao Fan, Hongfei Zhao, Yumei Shi, Yang |
author_sort | Gao, Jiayao |
collection | PubMed |
description | With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then proposed to provide a more secured identification approach compared to traditional security methods. Such solutions take hardware-level characteristics as device fingerprints to mitigate the risk of pre-shared key leakage and lower computational complexity. However, the existing studies suffer from problems such as location dependence. In this study, we have proposed a novel scheme for further exploiting the spectrogram and the carrier frequency offset (CFO) as identification sources. A convolutional neural network (CNN) is chosen as the classifier. The scheme addressed the location-dependence problem in the existing identification schemes. Experimental evaluations with data collected in the real world have indicated that the proposed approach can achieve 80% accuracy even if the training and testing data are collected on different days and at different locations, which is 13% higher than state-of-the-art approaches. |
format | Online Article Text |
id | pubmed-10453519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104535192023-08-26 Leveraging Deep Learning for IoT Transceiver Identification Gao, Jiayao Fan, Hongfei Zhao, Yumei Shi, Yang Entropy (Basel) Article With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then proposed to provide a more secured identification approach compared to traditional security methods. Such solutions take hardware-level characteristics as device fingerprints to mitigate the risk of pre-shared key leakage and lower computational complexity. However, the existing studies suffer from problems such as location dependence. In this study, we have proposed a novel scheme for further exploiting the spectrogram and the carrier frequency offset (CFO) as identification sources. A convolutional neural network (CNN) is chosen as the classifier. The scheme addressed the location-dependence problem in the existing identification schemes. Experimental evaluations with data collected in the real world have indicated that the proposed approach can achieve 80% accuracy even if the training and testing data are collected on different days and at different locations, which is 13% higher than state-of-the-art approaches. MDPI 2023-08-10 /pmc/articles/PMC10453519/ /pubmed/37628220 http://dx.doi.org/10.3390/e25081191 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Jiayao Fan, Hongfei Zhao, Yumei Shi, Yang Leveraging Deep Learning for IoT Transceiver Identification |
title | Leveraging Deep Learning for IoT Transceiver Identification |
title_full | Leveraging Deep Learning for IoT Transceiver Identification |
title_fullStr | Leveraging Deep Learning for IoT Transceiver Identification |
title_full_unstemmed | Leveraging Deep Learning for IoT Transceiver Identification |
title_short | Leveraging Deep Learning for IoT Transceiver Identification |
title_sort | leveraging deep learning for iot transceiver identification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453519/ https://www.ncbi.nlm.nih.gov/pubmed/37628220 http://dx.doi.org/10.3390/e25081191 |
work_keys_str_mv | AT gaojiayao leveragingdeeplearningforiottransceiveridentification AT fanhongfei leveragingdeeplearningforiottransceiveridentification AT zhaoyumei leveragingdeeplearningforiottransceiveridentification AT shiyang leveragingdeeplearningforiottransceiveridentification |