Open Set RF Fingerprinting using Generative Outlier Augmentation
This addresses the costly challenge of acquiring unauthorized transmitters for security applications in wireless networks, though it is incremental by building on existing generative methods.
The paper tackles the problem of open set RF fingerprinting for device identification, where classifiers must reject unauthorized transmitters without needing real samples from them, and shows that using generative deep learning for data augmentation significantly increases classification accuracy, especially with small authorized sets.
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.