The application of the Symbolic Aggregate Approximation algorithm (SAX) to radio frequency fingerprinting of IoT devices

Radio Frequency (RF) fingerprinting is the problem of identifying and authenticating an electronic device through its radio frequency emissions. These emissions contain intrinsic features of the device itself. RF fingerprinting can be used to enhance the security of wireless networks since the fingerprints provide a form of authentication complementing other measures. RF-based authentication turns out to be of practical use in security applications as long as the RF fingerprinting delivers high identification and verification accuracy, and the whole process is computationally efficient. In this paper, we investigate a novel approach to RF fingerprinting based on the application to time series of the Symbolic Aggregate Approximation algorithm (SAX). This is a compression scheme known to be time efficient and, although it has been applied to many domains, it has so far never been investigated in the problem of RF fingerprinting. We demonstrate that a SAX-based approach provides a very high identification accuracy (over 99%), and turns out to be attractive, as compared to classification without SAX, from both a computational standpoint and its robustness to noise.