  Due to imperfections in transmitters' hardware, wireless signals can be used
to verify their identity in an authorization system. While deep learning was
proposed for transmitter identification, the majority of the work has focused
on classification among a closed set of transmitters. Malicious transmitters
outside this closed set will be misclassified, jeopardizing the authorization
system. In this paper, we consider the problem of recognizing authorized
transmitters and rejecting new transmitters. To address this problem, we adapt
the most prominent approaches from the open set recognition and anomaly
detection literature to the problem. We study how these approaches scale with
the required number of authorized transmitters. We propose using a known set of
unauthorized transmitters to assist the training and study its impact. The
evaluation procedure takes into consideration that some transmitters might be
more similar than others and nuances these effects. The robustness of the RF
authorization with respect to temporal changes in fingerprints is also
considered in the evaluation. When using 10 authorized and 50 known
unauthorized WiFi transmitters from a publicly accessible testbed, we were able
to achieve an outlier detection accuracy of 98% on the same day test set and
80% on the different day test set.
