LAUSR

For small, inexpensive, and power-constrained IoT devices, Radiofrequency fingerprinting (RF-fingerprinting) has emerged as a cost-effective security solution. Robustness and permanence of the RF-fingerprints (RFFs) are major challenges since this solution’s inception. This is due to domain-related complications such as environmental effects and time-varying device-related perturbations. Since data from domains have divergent distributions, blindly plugging in Machine learning algorithms can overfit domain-related residuals rather than the fingerprint. Recent popular methods like blind channel equalization-based solutions only partially solve this problem while adversely affecting the RFF’s user capacity. Our paper presents a solution to overcome the domain generalization of these computationally intensive feature mining methods in a real-world wireless domain while retaining the fingerprints’ richness. We perform a reverse analysis of a typical RFIC and create a parametric RF-impairment distribution model currently missing in the literature. Then, we use this model to tailor a knowledge-based parametric signal processing and conditioning method, which would create an optimum signal representation of the RFF for ML algorithms. Additionally, our method can significantly reduce the dimensionality of the data needed to train the ML algorithms, eliminate noise, and simplify the classifier needed for RF-fingerprinting. We present our results after evaluation using real-world cross-domain experiments under varying domain conditions with COTS IoT microchips (SX1276).