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

arXiv: https://arxiv.org/abs/1901.05914 · HTML: https://mlsanity.com/paper/1901.05914/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning](https://mlsanity.com/paper/2204.07614/) (2022)

## Abstract (arXiv)

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 mathbfF(·), for device identification. mathbfF(·) 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 FID. Our proposed function model characterizes the unique physical-layer process of a device that is independent of the transmitted data, and hence, our system FID is data-independent and thus resilient against signal replay attacks. Modeling and further separating channel effects from the function model makes FID channel-robust. We evaluate FID on thousands of random signal packets from 33 different devices in different environments and scenarios, and the overall identification accuracy is over 99%.