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arXiv · 1907.08809

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Jiabao Yu·Aiqun Hu·Fen Zhou·Yuexiu Xing·Yi Yu·Guyue Li·Linning Peng
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Abstract

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising AutoEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.

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Jiabao Yu, Aiqun Hu, Fen Zhou, Yuexiu Xing, Yi Yu, Guyue Li, Linning Peng. 2019-07-20. Radio Frequency Fingerprint Identification Based on Denoising Autoencoders. https://arxiv.org/abs/1907.08809

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