In wireless networks, MAC-address spoofing is a common attack that allows an adversary to gain access to the system. To circumvent this threat, previous work has focused on classifying wireless signals using a “physical fingerprint”, i.e., changes to the signal caused by physical differences in the individual wireless chips. Instead of relying on MAC addresses for admission control, fingerprinting allows devices to be classified and then granted access. In many network settings, the activity of legitimate devices—those devices that should be granted access— may be dynamic over time. Consequently, when faced with a device that comes online, a robust fingerprinting scheme must quickly identify the device as legitimate using the pre-existing classification, and meanwhile identify and group those unauthorized devices based on their signals. This paper presents a two-stage Zero-Shot Learning (ZSL) approach to classify a received signal originating from either a legitimate or unauthorized device. In particular, during the training stage, a classifier is trained for classifying legitimate devices. The classifier learns discriminative features and the outlier detector uses these features to classify whether a new signature is an outlier. Then, during the testing stage, an online clustering method is applied for grouping those identified unauthorized devices. Our approach allows 42% of unauthorized devices to be identified as unauthorized and correctly clustered.