  RF data-driven device fingerprinting through the use of deep learning has
recently surfaced as a possible method for enabling secure device
identification and authentication. Traditional approaches are commonly
susceptible to the domain adaptation problem where a model trained on data
collected under one domain performs badly when tested on data collected under a
different domain. Some examples of a domain change include varying the location
or environment of the device and varying the time or day of the data
collection. In this work, we propose using multifractal analysis and the
variance fractal dimension trajectory (VFDT) as a data representation input to
the deep neural network to extract device fingerprints that are domain
generalizable. We analyze the effectiveness of the proposed VFDT representation
in detecting device-specific signatures from hardware-impaired IQ (in-phase and
quadrature) signals, and we evaluate its robustness in real-world settings,
using an experimental testbed of 30 WiFi-enabled Pycom devices. Our
experimental results show that the proposed VFDT representation improves the
scalability, robustness and generalizability of the deep learning models
significantly compared to when using IQ data samples.
