Deep Learning Based Wireless Device Identification Using RF Fingerprint

The wireless network physical layer identity authentication technology has received more and more attention due to its strong resistance to camouflage attacks. This work studies a physical layer method that uses radio frequency fingerprints to distinguish legitimate devices from illegal devices. This paper proposes the constellation trajectory generated by the fusion of three different length differential intervals of short, medium, and long as the device fingerprint to solve the problem that the traditional single differential interval constellation trajectory can not take into account the frequency offset resolution and the application range of frequency offset. The experimental results show that the fingerprint recognition accuracy of devices using multiple differential interval constellation trajectories is better than traditional methods. On this basis, we propose a radio frequency fingerprint identification method based on the twin neural network. Unlike most previous studies that model the identification problem as a classification problem, the algorithm proposed in this paper detects whether the device under test is a legitimate user device and can authenticate the legal device identity. The results show that the radio frequency fingerprint identification method for wireless devices based on the twin neural network can effectively and accurately identify legal users and illegal users.