  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.
