For small, inexpensive, and power-constrained IoT devices, Radiofrequency fingerprinting (RF-fingerprinting) has emerged as a cost-effective security solution.Robustnessandpermanenceof 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 overfitdomain-related residualsrather 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 thedomain generalizationof 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).

In Internet of Things, where billions of devices with limited resources are communicating with each other, security has become a major stumbling block affecting the progress of this technology. Existing authentication schemes based on digital signatures have overhead costs associated with them in terms of computation time, battery power, bandwidth, memory, and related hardware costs. Radio frequency fingerprint (RFF), utilizing the unique device-based information, can be a promising solution for IoT. However, traditional RFFs have become obsolete because of low reliability and reduced user capability. Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead. The injection of fingerprint is implemented using a low cost metasurface developed and fabricated in our lab, which is designed to make small but detectable perturbations in the specific frequency band in which the IoT devices are communicating. We have conducted comprehensive system evaluations including distance, orientation, multiple channels where the feasibility, effectiveness, and reliability of these fingerprints are validated. The proposed MeRFFI system can be easily integrated into the existing authentication schemes. The security vulnerabilities are analyzed for some of the most threatening wireless physical layer-based attacks.