WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research

TL;DR

This paper introduces WIDEFT, a publicly available dataset of radio frequency signals from 138 devices, enabling research on device fingerprinting based on hardware-induced signal variations.

Contribution

It provides the first comprehensive, publicly accessible RF signal corpus for wireless device fingerprinting research, along with baseline evaluation results.

Findings

Dataset includes 13,800 signal bursts from 138 devices.

Baseline classifiers achieve promising accuracy in device identification.

Demonstrates utility of the dataset for future research.

Abstract

Wireless network security may be improved by identifying networked devices via traits that are tied to hardware differences, typically related to unique variations introduced in the manufacturing process. One way these variations manifest is through unique transient events when a radio transmitter is activated or deactivated. Features extracted from these signal bursts have in some cases, shown to provide a unique "fingerprint" for a wireless device. However, only recently have researchers made such data available for research and comparison. Herein, we describe a publicly-available corpus of radio frequency signals that can be used for wireless device fingerprint research. The WIDEFT corpus contains signal bursts from 138 unique devices (100 bursts per device), including Bluetooth- and WiFi-enabled devices, from 79 unique models. Additionally, to demonstrate the utility of the WIDEFT corpus, we provide four baseline evaluations using a minimal subset of previously-proposed features and a simple ensemble classifier.