A Data-Independent Radio Frequency Fingerprint Extraction Scheme
Yang Yang, Aiqun Hu, Yuexiu Xing, Jiabao Yu, Zhen Zhang
Abstract
Radio frequency fingerprint (RFF) has been utilized to mitigate spoofing attacks in open wireless environments, making use of the inherent characteristics of hardware. However, most existing RFF technologies are data-dependent, e.g., based on preambles or synchronization sequences. In this letter, we propose a novel data-independent RFF extraction scheme, called Least mean square-based Adaptive Filter and Stacking, abbreviated as LAFS, that is implemented on random data segments, like communication data. Intuitively, we extract converged tap coefficients as RFF by minimizing the divergence between the desired signal and the demodulated reference signal. To further improve the effect, we introduce a tap coefficient stacking (TSC) technique to stabilize the RFF. Our experiment on ZigBee devices shows that the proposed LAFS method successfully identifies transmitters with 98.9% accuracy at 10 dB by stacking 25 times.