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Open AccessArticle

A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches

1
Military Technical Academy, Department of Communications and Military Electronic Systems, 050141 Bucharest, Romania
2
Gipsa-lab, Université Grenoble Alpes, 38402 Grenoble, France
3
Grenoble INP, LCIS, Université Grenoble Alpes, 26902 Valence, France
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6385; https://doi.org/10.3390/s20216385
Received: 1 October 2020 / Revised: 5 November 2020 / Accepted: 6 November 2020 / Published: 9 November 2020
(This article belongs to the Special Issue Intelligent and Adaptive Security in Internet of Things)
In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss. View Full-Text
Keywords: chipless RFID tags; classification; authentication; machine learning; electromagnetic signature; data augmentation; python; keras chipless RFID tags; classification; authentication; machine learning; electromagnetic signature; data augmentation; python; keras
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MDPI and ACS Style

Nastasiu, D.; Scripcaru, R.; Digulescu, A.; Ioana, C.; De Amorim, R., Jr.; Barbot, N.; Siragusa, R.; Perret, E.; Popescu, F. A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches. Sensors 2020, 20, 6385. https://doi.org/10.3390/s20216385

AMA Style

Nastasiu D, Scripcaru R, Digulescu A, Ioana C, De Amorim R Jr., Barbot N, Siragusa R, Perret E, Popescu F. A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches. Sensors. 2020; 20(21):6385. https://doi.org/10.3390/s20216385

Chicago/Turabian Style

Nastasiu, Dragoș; Scripcaru, Răzvan; Digulescu, Angela; Ioana, Cornel; De Amorim, Raymundo, Jr.; Barbot, Nicolas; Siragusa, Romain; Perret, Etienne; Popescu, Florin. 2020. "A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches" Sensors 20, no. 21: 6385. https://doi.org/10.3390/s20216385

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