sesa 21(29): e5

Research Article

Device Authentication Codes based on RF Fingerprinting using Deep Learning

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  • @ARTICLE{10.4108/eai.30-11-2021.172305,
        author={Joshua Bassey and Xiangfang Li and Lijun Qian},
        title={Device Authentication Codes based on RF Fingerprinting using Deep Learning},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={8},
        number={29},
        publisher={EAI},
        journal_a={SESA},
        year={2021},
        month={11},
        keywords={RF fingerprinting, Device Authentication, Deep Learning, Internet of Things, autoencoder, Kolmogorov-Smirnov (K-S) test},
        doi={10.4108/eai.30-11-2021.172305}
    }
    
  • Joshua Bassey
    Xiangfang Li
    Lijun Qian
    Year: 2021
    Device Authentication Codes based on RF Fingerprinting using Deep Learning
    SESA
    EAI
    DOI: 10.4108/eai.30-11-2021.172305
Joshua Bassey1,*, Xiangfang Li1, Lijun Qian1
  • 1: Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT), Department of Electrical and Computer Engineering, Prairie View A&M University, Texas A&M University System, Prairie View, TX 77446, USA
*Contact email: joshuababbs1@gmail.com

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, an information-theoretic method, feature learning, and the 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 each individual device. Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the receiver and the DAC in the received message, and the result will determine whether the device of interest is an intruder. We validate this concept on two experimentally collected RF traces from six ZigBee devices and five universal software defined radio peripheral devices, respectively. The traces span a range of Signal-to-Noise Ratio by varying locations, mobility of the devices, 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 each wireless device of interest and can be used to identify radio frequency devices. Furthermore, the proposed method does not need the RF traces of the intruder during model training to be able to identify devices not seen during training, which makes it practical.