Real-world Commercial WiFi and Bluetooth Dataset for RF Fingerprinting

A real-world radio frequency (RF) fingerprinting dataset for commercial off-the-shelf (COTS) Bluetooth and WiFi emitters under challenging testbed setups is presented in this dataset. The chipsets within the devices (2 laptops and 8 commercial chips) are WiFi-Bluetooth combo transceivers. The emissions are captured with a National Instruments Ettus USRP X300 radio outfitted with a UBX160 daughterboard and a VERT2450 antenna. The receiver is tuned to record a 66.67 MHz bandwidth of the spectrum centered at the 2.414 GHz frequency. This is a first-of-its-kind dataset for fingerprinting WiFi-Bluetooth combo chipsets that are commonly found in today's IoT era. The dataset is split into two for WiFi and Bluetooth - Day1 and Day2 - each of which is recorded in a different time frame but under the same receiver settings to enable a critical generalization test of the trained deep learning (DL) model. The authors suggest training the DL model with the Day1 dataset with recordings under varied Bluetooth and WiFi signal strengths followed by evaluating the generalization capability of the model with the Day2 dataset which is a challenging and different setup compared to Day1. This evaluation will validate the realistic deployment capability of the trained DL model. The dataset follows the SigMF specifications with certain field extensions to facilitate the fingerprinting application and include additional metadata fields. Each capture is of length 40 Mega Samples (MS) and is associated with a JSON metadata file.

搜集汇总

背景与挑战

背景概述

这是一个真实世界的射频指纹数据集，专注于商用WiFi和蓝牙组合芯片的发射器识别。数据集包含两个时间段（Day1和Day2）的独立记录，分别针对蓝牙（在不同距离下）和WiFi（在不同发射功率下）的无线信号，旨在测试深度学习模型的泛化能力。数据以SigMF规范存储，包括IQ样本和JSON元数据文件，适用于人工智能、物联网安全等研究领域。