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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 21; p. 6385
• Main Authors: Nastasiu, Dragoș, Scripcaru, Răzvan, Digulescu, Angela, Ioana, Cornel, De Amorim, Raymundo, Barbot, Nicolas, Siragusa, Romain, Perret, Etienne, Popescu, Florin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2020; MDPI
• Subjects: Access control; Artificial Intelligence; Authentication; Chipless machining; chipless RFID tags; Classification; Computer Science; Counterfeiting; Cryptography and Security; data augmentation; Discriminant analysis; electromagnetic signature; Engineering Sciences; Fingerprinting; Fingerprints; Machine learning; Manufacturing; Methods; Neural networks; Radio frequency identification; Search algorithms; Signal and Image processing; Supply chains; Tags
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20216385

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.