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

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