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- Title
Comparison of techniques for radiometric identification based on deep convolutional neural networks.
- Authors
Baldini, G.; Gentile, C.; Giuliani, R.; Steri, G.
- Abstract
The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF-to-image techniques show that on their datasets the wavelet-based approach outperforms other approaches, also in the presence of white Gaussian noise.
- Subjects
RADIOMETRY; RADIO frequency; WIRELESS communications; RANDOM noise theory
- Publication
Electronics Letters (Wiley-Blackwell), 2019, Vol 55, Issue 2, p90
- ISSN
0013-5194
- Publication type
Article
- DOI
10.1049/el.2018.6229