Wi-Fi RFFI Signal-Level Synthetic Traces

This resource provides synthetic Wi-Fi signal traces generated at the bit level with hardware impairments, designed for RFFI-based impersonation detection research.

Overview

The dataset supports the study of radio frequency fingerprinting identification (RFFI) for detecting device impersonation in Wi-Fi networks. Signals are generated at the bit level using 16-QAM and OFDM modulation, with device-specific hardware impairments including CFO, IQ imbalance, phase noise, and non-linear distortions. Transmission effects are simulated using additive white Gaussian noise and Rayleigh fading channels.

What is included

IQ traces for 4 legitimate devices under data/legitimate/
IQ traces for 4 attacker devices under data/attacker/, each impersonating a corresponding legitimate device
hardware impairments: CFO, IQ imbalance, phase noise, non-linear distortion
channel effects: AWGN and Rayleigh fading
scripts/analyze.py for feature extraction and XGBoost-based device classification

Dataset context

The dataset was generated to evaluate unsupervised anomaly detection for Wi-Fi impersonation attacks. Each legitimate device has unique hardware fingerprint parameters assigned via a separable parameter grid. Attacker devices share the MAC address of their target legitimate device while retaining distinct hardware characteristics, simulating realistic impersonation scenarios.

Each .npy file contains 1000 signal samples per device with shape (1000, signal_length).

Why it matters

This dataset offers a reproducible setting for evaluating RFFI-based anomaly detection under controlled hardware impairments and channel conditions.

Access

Dataset and code: GitHub
License: CC BY 4.0

Example result

This preview shows the XGBoost classification confusion matrix on the synthetic dataset, illustrating the separability of device-specific RF fingerprints across legitimate and attacker devices.

Citation

If you use this resource, please cite the related publication above.