PROJECT TITLE :

Identification of Wireless Devices of Users Who Actively Fake Their RF Fingerprints With Artificial Data Distortion

ABSTRACT:

Variations within the RF chain of radio transmitters caused by imperfections of producing processes can be used as a signature to uniquely associate wireless devices with a given transmission. In our previous work, we have a tendency to proposed a model-based approach that permits for identification of wireless devices based on signatures obtained with time domain analysis of a pair of received and decoded signals. Here, we have a tendency to consider sturdy adversaries who intentionally introduce distortions to the data symbols before the symbols are exposed to the transmitter's inherent nonlinearities, with the intention of faking the signatures of their devices whereas still allowing for proper information decoding. The tactic proposed during this work is based on spectral analysis and on the observation that nonlinear elements cause in-band distortion and spectral regrowth of the signal that is passionate about the parameters of the nonlinearity. Hence, by analysis of the in-band distortion of the spectrum similarly as the spectral regrowth, we show that wireless devices can be successfully identified even when the users are digitally modifying their data symbols. The utility of the proposed identification approach is demonstrated with simulations based mostly on parameters obtained from the measurements of commercially utilized WLAN RF transmitters