On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method.
| Autor: | Shi D; Laboratory LISITE, Institut Supérieur D'électronique de Paris, 75006 Paris, France., Zhang X; Laboratory LISITE, Institut Supérieur D'électronique de Paris, 75006 Paris, France., Shi L; Laboratory LISITE, Institut Supérieur D'électronique de Paris, 75006 Paris, France., Vladimirescu A; Laboratory LISITE, Institut Supérieur D'électronique de Paris, 75006 Paris, France., Mazurczyk W; Institute of Computer Scence, Warsaw University of Technology, 00-665 Warsaw, Poland., Cabaj K; Institute of Computer Scence, Warsaw University of Technology, 00-665 Warsaw, Poland., Meunier B; Department of Electronic and Computer Engineering, Brunel University, Uxbridge UB8 3PN, UK., Ali K; Department of Electronic and Computer Engineering, Brunel University, Uxbridge UB8 3PN, UK., Cosmas J; Department of Electronic and Computer Engineering, Brunel University, Uxbridge UB8 3PN, UK., Zhang Y; School of Engineering, University of Leicester, Leicester LE1 7RH, UK. |
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| Jazyk: | angličtina |
| Zdroj: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Feb 22; Vol. 21 (4). Date of Electronic Publication: 2021 Feb 22. |
| DOI: | 10.3390/s21041515 |
| Abstrakt: | In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%. |
| Databáze: | MEDLINE |
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