  RF devices can be identified by unique imperfections embedded in the signals
they transmit called RF fingerprints. The closed set classification of such
devices, where the identification must be made among an authorized set of
transmitters, has been well explored. However, the much more difficult problem
of open set classification, where the classifier needs to reject unauthorized
transmitters while recognizing authorized transmitters, has only been recently
visited. So far, efforts at open set classification have largely relied on the
utilization of signal samples captured from a known set of unauthorized
transmitters to aid the classifier learn unauthorized transmitter fingerprints.
Since acquiring new transmitters to use as known transmitters is highly
expensive, we propose to use generative deep learning methods to emulate
unauthorized signal samples for the augmentation of training datasets. We
develop two different data augmentation techniques, one that exploits a limited
number of known unauthorized transmitters and the other that does not require
any unauthorized transmitters. Experiments conducted on a dataset captured from
a WiFi testbed indicate that data augmentation allows for significant increases
in open set classification accuracy, especially when the authorized set is
small.
