Radio Frequency Fingerprint Identification Based on Denoising Autoencoders
This work addresses security challenges for low-power IoT devices by enhancing passive authentication in noisy environments, representing an incremental improvement over existing deep learning methods.
The paper tackles the problem of low identification accuracy for Radio Frequency Fingerprinting (RFF) in low SNR scenarios for IoT security by proposing a Partially Stacking-based Convolutional Denoising AutoEncoder (PSC-DAE), which improves accuracy by 14% to 23.5% at SNRs from -10 dB to 5 dB and achieves 97.5% at 10 dB.
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%.