Improving WiFi CSI Fingerprinting with IQ Samples

TL;DR

This paper introduces CSI2Q, a novel WiFi CSI fingerprinting system that transforms CSI into time-domain signals to achieve accuracy comparable to IQ-based methods, enhancing device authentication.

Contribution

CSI2Q is the first approach to convert CSI into a feature space similar to IQ samples, enabling lightweight and accurate RF fingerprinting without dedicated hardware.

Findings

Recognition accuracy improved from 76% to 91% on synthetic data.

Recognition accuracy improved from 67% to 82% on real data.

CSI2Q outperforms existing CSI-based fingerprinting methods.

Abstract

Identity authentication is crucial for ensuring the information security of wireless communication. Radio frequency (RF) fingerprinting techniques provide a prom-ising supplement to cryptography-based authentication 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 in-formation (CSI) by commercial WiFi devices for lightweight RF fingerprinting, but they mainly focus on eliminating channel interference and cannot address the challenges of coarse granularity and information loss of CSI measurements. To overcome these challenges, we propose CSI2Q, a novel CSI fingerprinting sys-tem that achieves comparable performance to IQ-based approaches. Instead of ex-tracting fingerprints directly from raw CSI measurements, CSI2Q first transforms them into time-domain signals that share the same feature space with IQ samples. Then, the distinct advantages of an IQ fingerprinting model in feature extraction are transferred to its CSI counterpart via an auxiliary training strategy. Finally, the trained CSI fingerprinting model is used to decide which device the sample under test comes from. We evaluate CSI2Q on both synthetic and real CSI datasets. On the synthetic dataset, our system can improve the recognition accuracy from 76% to 91%. On the real dataset, CSI2Q boosts the accuracy from 67% to 82%.