FID: Function Modeling-based Data-Independent and Channel-Robust Physical-Layer Identification

Trusted identification is critical to secure IoT devices. However, the limited memory and computation power of low-end IoT devices prevent the direct usage of conventional identification systems. RF fingerprinting is a promising technique to identify low-end IoT devices since it only requires the RF signals that most IoT devices can produce for communication. However, most existing RF fingerprinting systems are data-dependent and/or not robust to impacts from wireless channels. To address the above problems, we propose to exploit the mathematical expression of the physical-layer process, regarded as a function T(·), for device identification. T(·) is not directly derivable, so we further propose a model to learn it and employ this function model as the device fingerprint in our system, namely TID. Our proposed function model characterizes the unique physical-layer process of a device that is independent of the transmitted data, and hence, our system TID is data-independent and thus resilient against signal replay attacks. Modeling and further separating channel effects from the function model makes TID channelrobust. We evaluate TID on thousands of random signal packets from 33 different devices in different environments and scenarios, and the overall identification accuracy is over 99%.