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RF fingerprinting leverages circuit-level variability of transmitters toidentify them using signals they send. Signals used for identification areimpacted by a wireless channel and receiver circuitry, creating additionalimpairments that can confuse transmitter identification. Eliminating theseimpairments or just evaluating them, requires data captured over a prolongedperiod of time, using many spatially separated transmitters and receivers. Inthis paper, we present WiSig; a large scale WiFi dataset containing 10 millionpackets captured from 174 off-the-shelf WiFi transmitters and 41 USRP receiversover 4 captures spanning a month. WiSig is publicly available, not just as rawcaptures, but as conveniently pre-processed subsets of limited size, along withthe scripts and examples. A preliminary evaluation performed using WiSig showsthat changing receivers, or using signals captured on a different day cansignificantly degrade a trained classifier's performance. While capturing dataover more days or more receivers limits the degradation, it is not alwaysfeasible and novel data-driven approaches are needed. WiSig provides the datato develop and evaluate these approaches towards channel and receiver agnostictransmitter fingerprinting.

WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting