Robust Stable Radiometric Fingerprinting for Wireless Devices

We introduce a new method for radiometric fingerprinting that detects the unique variations in the antenna, oscillator properties, as well as the digital and analog interfaces of the radio by passively monitoring the radio packets. Several individual identifiers are used for extracting the unique physical characteristics of the radio, including the frequency offset, modulated phase offset, in-phase/quadrature-phase offset from the origin, and magnitude. Our method provides stable and robust identification by developing individual identifiers (classifiers) that may each be weak (i.e., incurring a high prediction error) but their committee can provide a strong classification technique. We use two methods for combining the classifiers: (1) weighted voting, and (2) maximum likelihood. Our hardware implementation and experimental evaluations over multiple radios demonstrate that our weighted voting approach can identify the radios with an average of 88% detection probability and an average of 12.8% probability of false alarm after testing only 5 frames. The probability of detection and probability of false alarms both rapidly improve by increasing the number of test frames.