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The accurate identification of wireless devices is critical for enabling automated network access monitoring and authenticated data communication in large-scale networks; e.g., IoT networks. RF fingerprinting has emerged as a potential solution for device identification by leveraging the transmitter unique manufacturing impairments of the RF components. Although deep learning is proven efficient in classifying devices based on the hardware impairments, trained models perform poorly due to channel variations. That is, although training and testing neural networks using data generated during the same period achieve reliable classification, testing them on data generated at different times degrades the accuracy substantially. To the best of our knowledge, we are the first to propose to leverage MIMO capabilities to mitigate the channel effect and provide a channelresilient device classification. For the proposed technique we show that, for Rayleigh channels, blind partial channel estimation enabled by MIMO increases the testing accuracy by up to 40% when the models are trained and tested over the same channel, and by up to 60% when the models are tested on a channel that is different from that used for training.
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WideScan: Exploiting Out-of-Band Distortion for Device Classification Using Deep Learning
Wireless device classification techniques play a vital role in supporting spectrum awareness applications, such as spectrum access policy enforcement and unauthorized network access monitoring. Recent works proposed to exploit distortions in the transmitted signals caused by hardware impairments of the devices to provide device identification and classification using deep learning. As technology advances, the manufacturing impairment variations among devices become extremely insignificant, and hence the need for more sophisticated device classification techniques becomes inescapable. This paper proposes a scalable, RF data-driven deep learning-based device classification technique that efficiently classifies transmitting radios from a large pool of bit-similar, high-end, high-performance devices with same hardware, protocol, and/or software configurations. Unlike existing techniques, the novelty of the proposed approach lies in exploiting both the in-band and out-of-band distortion information, caused by inherent hardware impairments, to enable scalable and accurate device classification. Using convolutional neural network (CNN) model for classification, our results show that the proposed technique substantially outperforms conventional approaches in terms of both classification accuracy and learning times. In our experiments, the testing accuracy obtained under the proposed technique is about 96% whereas that obtained under the conventional approach is only about 50% when the devices exhibit very similar hardware impairments. The proposed technique can be implemented with minimum receiver design tuning, as radio technologies, such as cognitive radios, can easily allow for both in-band and out-of band sampling.
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- Award ID(s):
- 2003273
- PAR ID:
- 10245790
- Date Published:
- Journal Name:
- 2020 IEEE Global Communications Conference
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOBECOM42002.2020.9348138
