Effect of the short time fourier transform on the classification of complex-valued mobile signals

Wireless devices identify themselves using media access control (MAC) addresses which can be easily intercepted and mimicked by an adversary. Mobile devices also have a unique physical fingerprint represented by perturbations in the frequency of broadcasted signals caused by differences in the manufacturing process of their hardware components. This unique fingerprint is much more difficult to mimic. The short time Fourier transform (STFT) is used to analyze how the frequency content of a signal changes over time, and may provide a better representation of mobile signals in order to detect their unique fingerprint. In this paper, we have collected wireless signals using the 802.11 a/g protocol, showing the effect on classification performance of applying the STFT when varying the choice of window lengths, augmenting the data with complex Gaussian noise, and concatenating STFTs of different frequency resolutions, achieving state-of-the-art performance of 99.94% accuracy in the process.