Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks

Abstract

With the increasing presence of cognitive radio networks as a means to address limited spectral resources, improved wireless security has become a necessity. In particular, the potential of a node to impersonate a licensed user demonstrates the need for techniques to authenticate a radio's true identity. In this paper, we use deep learning to detect physical-layer attributes for the identification of cognitive radio devices, and demonstrate the performance of our method on a set of IEEE 802.15.4 devices. Our method is based on the empirical principle that manufacturing variability among wireless transmitters that conform to the same standard creates unique, repeatable signatures in each transmission, which can then be used as a fingerprint for device identification and verification. We develop a framework for training a convolutional neural network using the time-domain complex baseband error signal and demonstrate 92.29% identification accuracy on a set of seven 2.4 GHz commercial ZigBee devices. We also demonstrate the robustness of our method over a wide range of signal-to-noise ratios.