A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches
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 f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/21/6385 |
| _version_ | 1797548467915063296 |
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| author | Dragoș Nastasiu Răzvan Scripcaru Angela Digulescu Cornel Ioana Raymundo De Amorim Nicolas Barbot Romain Siragusa Etienne Perret Florin Popescu |
| author_facet | Dragoș Nastasiu Răzvan Scripcaru Angela Digulescu Cornel Ioana Raymundo De Amorim Nicolas Barbot Romain Siragusa Etienne Perret Florin Popescu |
| author_sort | Dragoș Nastasiu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:59:45Z |
| format | Article |
| id | doaj.art-5704924544354270be62062a31c1502b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:59:45Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5704924544354270be62062a31c1502b2023-11-20T20:17:00ZengMDPI AGSensors1424-82202020-11-012021638510.3390/s20216385A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning ApproachesDragoș Nastasiu0Răzvan Scripcaru1Angela Digulescu2Cornel Ioana3Raymundo De Amorim4Nicolas Barbot5Romain Siragusa6Etienne Perret7Florin Popescu8Military Technical Academy, Department of Communications and Military Electronic Systems, 050141 Bucharest, RomaniaMilitary Technical Academy, Department of Communications and Military Electronic Systems, 050141 Bucharest, RomaniaMilitary Technical Academy, Department of Communications and Military Electronic Systems, 050141 Bucharest, RomaniaGipsa-lab, Université Grenoble Alpes, 38402 Grenoble, FranceGrenoble INP, LCIS, Université Grenoble Alpes, 26902 Valence, FranceGrenoble INP, LCIS, Université Grenoble Alpes, 26902 Valence, FranceGrenoble INP, LCIS, Université Grenoble Alpes, 26902 Valence, FranceGrenoble INP, LCIS, Université Grenoble Alpes, 26902 Valence, FranceMilitary Technical Academy, Department of Communications and Military Electronic Systems, 050141 Bucharest, RomaniaIn 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.https://www.mdpi.com/1424-8220/20/21/6385chipless RFID tagsclassificationauthenticationmachine learningelectromagnetic signaturedata augmentation |
| spellingShingle | Dragoș Nastasiu Răzvan Scripcaru Angela Digulescu Cornel Ioana Raymundo De Amorim Nicolas Barbot Romain Siragusa Etienne Perret Florin Popescu A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches Sensors chipless RFID tags classification authentication machine learning electromagnetic signature data augmentation |
| title | A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches |
| title_full | A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches |
| title_fullStr | A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches |
| title_full_unstemmed | A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches |
| title_short | A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches |
| title_sort | new method of secure authentication based on electromagnetic signatures of chipless rfid tags and machine learning approaches |
| topic | chipless RFID tags classification authentication machine learning electromagnetic signature data augmentation |
| url | https://www.mdpi.com/1424-8220/20/21/6385 |
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