Deep RF Device Fingerprinting by Semi-Supervised Learning with Meta Pseudo Time-Frequency Labels
Zhanyi Ren, Pinyi Ren, Tiantian Zhang
Abstract
With the ever-increasing growth of wireless communication technologies and the proliferation of the Internet of Things (IoT), intelligent authentication systems to distinguish legitimate devices are of vital importance. These years, deep learning based authentication algorithms have achieved considerable precision by leveraging radio frequency (RF) fingerprints. However, these methods depending on massive labeled data are difficult to apply on large-scale devices identification. In this paper, we propose a novel method using semi-supervised deep learning employing RF fingerprinting with meta pseudo time-frequency labels to improve identification performance in small-scale labeled datasets. We demonstrate how the scale of datasets and the proportion of labeled data influence the accuracy of identification by analyzing a dataset of 40 GB real Long-Term-Evolution (LTE) mobile phone’s raw signals. Experimental results show that compared with the non-convergence of traditional supervised learning with 100 labeled data, our method can achieve the authentication accuracy of 99.86% with the same labeled data. Moreover, when using the same scale of training datasets and labeled half, our approach could obtain authentication accuracy higher than traditional supervised learning. And even 1% labeled data of 900 training data, this method can still obtain the accuracy of 91.25%.