Device Authentication Codes based on RF Fingerprinting using Deep Learning

• URL: http://arxiv.org/abs/2004.08742v1
• Date: Sun, 19 Apr 2020 01:50:29 GMT
• Title: Device Authentication Codes based on RF Fingerprinting using Deep Learning
• Authors: Joshua Bassey, Xiangfang Li, Lijun Qian
• Abstract summary: Device Authentication Code (DAC) is a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures. We show that DAC is able to prevent device impersonation by extracting salient features that are unique to any wireless device of interest.
• Score: 2.980018103007841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures. The proposed DAC is based on RF fingerprinting, information theoretic method, feature learning, and discriminatory power of deep learning. Specifically, an autoencoder is used to automatically extract features from the RF traces, and the reconstruction error is used as the DAC and this DAC is unique to the device and the particular message of interest. Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the autoencoder and the received message, and the result will determine whether the device of interest belongs to an authorized user. We validate this concept on two experimentally collected RF traces from six ZigBee and five universal software defined radio peripheral (USRP) devices, respectively. The traces span a range of Signalto- Noise Ratio by varying locations and mobility of the devices and channel interference and noise to ensure robustness of the model. Experimental results demonstrate that DAC is able to prevent device impersonation by extracting salient features that are unique to any wireless device of interest and can be used to identify RF devices. Furthermore, the proposed method does not need the RF traces of the intruder during model training yet be able to identify devices not seen during training, which makes it practical.

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