This conference presentation discusses the use of Short Time Fourier Transform to analyze how the frequency content of a signal changes over time.
In this presentation, the authors discuss the unique physical fingerprints of mobile devices, which are represented by perturbations in the frequency of broadcasted signals caused by differences in the manufacturing process of their hardware components. They point out that the unique fingerprint of mobile devices is much harder to mimic than that of wireless devices which can be mimicked using media access control (MAC) addresses, and suggest that Short Time Fourier Transform (STFT) may be used to analyze how the frequency content of a signal changes over time, and that it can provide a better representation of mobile signals in order to detect their unique fingerprint. The authors present the results of their data collection of wireless signals using the 802.11 a/g protocol, which shows 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, which achieved state-of-the-art performance of 99.94% accuracy.