Enhancing WiFi CSI Fingerprinting: A Deep Auxiliary Learning Approach

TL;DR

This paper introduces CSI2Q, a deep auxiliary learning-based system that transforms WiFi CSI into a feature space similar to IQ samples, significantly improving device fingerprinting accuracy in open-world environments.

Contribution

CSI2Q is the first system to transfer knowledge from IQ-based fingerprinting to CSI, enhancing accuracy without requiring dedicated RF equipment.

Findings

Achieves at least 16% accuracy improvement on synthetic data

Improves accuracy by 20% on in-lab datasets

Enhances accuracy by 17% in real-world scenarios

Abstract

Radio frequency (RF) fingerprinting techniques provide a promising supplement to cryptography-based approaches but rely on dedicated equipment to capture in-phase and quadrature (IQ) samples, hindering their wide adoption. Recent advances advocate easily obtainable channel state information (CSI) by commercial WiFi devices for lightweight RF fingerprinting, while falling short in addressing the challenges of coarse granularity of CSI measurements in an open-world setting. In this paper, we propose CSI2Q, a novel CSI fingerprinting system that achieves comparable performance to IQ-based approaches. Instead of extracting fingerprints directly from raw CSI measurements, CSI2Q first transforms frequency-domain CSI measurements into time-domain signals that share the same feature space with IQ samples. Then, we employ a deep auxiliary learning strategy to transfer useful knowledge from an IQ fingerprinting model to the CSI counterpart. Finally, the trained CSI model is combined with an OpenMax function to estimate the likelihood of unknown ones. We evaluate CSI2Q on one synthetic CSI dataset involving 85 devices and two real CSI datasets, including 10 and 25 WiFi routers, respectively. Our system achieves accuracy increases of at least 16% on the synthetic CSI dataset, 20% on the in-lab CSI dataset, and 17% on the in-the-wild CSI dataset.