  Wireless device classification techniques play a key role in promoting
emerging wireless applications such as allowing spectrum regulatory agencies to
enforce their access policies and enabling network administrators to control
access and prevent impersonation attacks to their wireless networks. Leveraging
spectrum distortions of transmitted RF signals, caused by transceiver hardware
impairments created during manufacture and assembly stages, to provide device
classification has been the focus of many recent works. These prior works
essentially apply deep learning to extract features of the devices from their
hardware-impaired signals and rely on feature variations across the devices to
distinguish devices from one another. As technology advances, the manufacturing
impairment variations across devices are becoming extremely insignificant,
making these prior classification approaches inaccurate. This paper proposes a
novel, deep learning-based technique that provides scalable and highly accurate
classification of wireless devices, even when the devices exhibit insignificant
variation across their hardware impairments and have the same hardware,
protocol, and software configurations. The novelty of the proposed technique
lies in leveraging both the {\em in-band} and {\em out-of-band} signal
distortion information by oversampling the captured signals at the receiver and
feeding IQ samples collected from the RF signals to a deep neural network for
classification. Using a convolutional neural network (CNN) model, we show that
our proposed technique, when applied to high-end, high-performance devices with
minimally distorted hardware, doubles the device classification accuracy when
compared to existing approaches.
