Deep-Learning-Aided RF Fingerprinting for NFC Relay Attack Detection

Abstract

Near field communication (NFC) has been a widely used radiofrequency identification (RFID) technology, credited to its convenience and security features. However, the transmitted signals can be easily eavesdropped or relayed in an open wireless channel. One of the challenges is relay attack, where an attacker simply relays the signal and bypasses encryption or other means in the application layer. Prior works on relay attack countermeasures have focused on distance-bounding protocols or ambient-based solutions. This paper focuses on ISO/IEC 14443-A and proposes an NFC relay detection method based on RF fingerprinting of transmitted wireless signals in the physical layer. To this end, we first designed and implemented two realizations of NFC relay attacks, wired and wireless relays, and built an SDR-based testbed. We collected the normal and relayed signals of four NFC tags, and the answer to request type A (ATQA) segments were selected for RF fingerprinting. The created dataset comprised 66,366 samples, with four tags’ normal and wired relayed signals and the wireless relayed signals. The dataset was then fed into a deep CNN for training. Finally, our experiment results showed that the method effectively distinguished normal and relayed signals with a high accuracy of 99%, confirming that RF fingerprinting can be a promising countermeasure to NFC relay attacks.