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CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi

Ruiqi Kong, He Chen

Abstract

This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed ``micro-CSI''. Through experimentation, we have found that the presence of micro-CSI can primarily be attributed to imperfections in the RF circuitry. Furthermore, this characteristic signal is detectable in WiFi 4/5/6 network interface cards (NICs). We have conducted further experiments to determine the most effective CSI collection configurations to stabilize micro-CSI. Yet, extracting micro-CSI for authentication purposes poses a significant challenge. This complexity arises from the fact that CSI measurements inherently include both micro-CSI and the distortions introduced by wireless channels. These two elements are intricately intertwined, making their separation non-trivial. To tackle this challenge, we have developed a signal space-based extraction technique for line-of-sight (LoS) scenarios, which can effectively separate the distortions caused by wireless channels and micro-CSI. Over the course of our comprehensive CSI data collection period extending beyond one year, we found that the extracted micro-CSI displays unique characteristics specific to each WiFi device and remains invariant over time. This establishes micro-CSI as a suitable candidate for device fingerprinting. Finally, we conduct a case study focusing on area access control for mobile robots. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate close to 99% with a false alarm rate of 0% in both static and mobile conditions when using 20 CSI measurements to construct one fingerprint.

CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi

Abstract

This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed ``micro-CSI''. Through experimentation, we have found that the presence of micro-CSI can primarily be attributed to imperfections in the RF circuitry. Furthermore, this characteristic signal is detectable in WiFi 4/5/6 network interface cards (NICs). We have conducted further experiments to determine the most effective CSI collection configurations to stabilize micro-CSI. Yet, extracting micro-CSI for authentication purposes poses a significant challenge. This complexity arises from the fact that CSI measurements inherently include both micro-CSI and the distortions introduced by wireless channels. These two elements are intricately intertwined, making their separation non-trivial. To tackle this challenge, we have developed a signal space-based extraction technique for line-of-sight (LoS) scenarios, which can effectively separate the distortions caused by wireless channels and micro-CSI. Over the course of our comprehensive CSI data collection period extending beyond one year, we found that the extracted micro-CSI displays unique characteristics specific to each WiFi device and remains invariant over time. This establishes micro-CSI as a suitable candidate for device fingerprinting. Finally, we conduct a case study focusing on area access control for mobile robots. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate close to 99% with a false alarm rate of 0% in both static and mobile conditions when using 20 CSI measurements to construct one fingerprint.
Paper Structure (22 sections, 5 equations, 14 figures, 3 tables, 1 algorithm)

This paper contains 22 sections, 5 equations, 14 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries and Observations
  4. Observations of Micro-CSI
  5. Micro-CSI Extraction
  6. Signal Acquisition
  7. ACK CSI Toolkit
  8. Signal Space-Based Micro-CSI Extraction
  9. Micro-CSI-based RF Fingerprinting
  10. Fingerprint Construction
  11. Noise Suppresion
  12. Outlier Elimination
  13. Evaluations of Fingerprint Robustness
  14. Fingerprint Matcher
  15. A Robotic Use Case
  16. ...and 7 more sections

Figures (14)

  • Figure 1: Micro-signals shown on CSI curves collected at different times and positions, and a similar pattern of micro-signals appears in each curve after suppressing the effects of random noise.
  • Figure 2: Amplitude and phase curves of the CSI received by different receiver ports of one NIC, where each curve is averaged over $100$ consecutive CSI measurements, which are collected under static conditions with minor CSI changes. Micro signals on each pair of amplitude and phase curves keep almost unchanged.
  • Figure 3: Extracted Micro-CSI from practical CSI measurement.
  • Figure 4: Three types of CSI measurements.
  • Figure 5: Uniqueness of amplitude of fingerprints of 8 NICs of various brands, where $N_{csi}=20, N_{rx}=4$.
  • ...and 9 more figures