Fine-grained Augmentation for RF Fingerprinting under Impaired Channels

Omer Melih Gul, Michel Kulhandjian, Burak Kantarci, Azzedine Touazi, Cliff Ellement, Claude D'Amours

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

Critical infrastructures such as connected and au-tonomous vehicles, are susceptible to cyber attacks due to their mission-critical deployment. To ensure security by design, radio frequency (RF)-based security is considered as an effective technique for a wirelessly monitored or actuated critical infrastructure. For this purpose, this paper proposes a novel augmentation-driven deep learning approach to analyze unique transmitter fingerprints to determine the legitimacy of a user device or transmitter. An RF fingerprinting model is susceptible to various channel and environmental conditions that impact the learning performance of a machine/deep learning model. As data gathering cannot be considered as a feasible alternative, efficient solutions that can tackle the impact of varying channels on learning performance are emergent. This work aims to shed light on the RF fingerprinting problem from a different angle when 4G, 5G and WiFi data samples are collected from different transmitters by proposing a fine-grained augmentation approach to improve the learning performance of a deep learning model. Numerical results point out the promising RF fingerprinting performance when training data are augmented in a waveform-specific fine-grained manner as fingerprinting accuracy (87.94%) under the previously presented TDL/CDL augmentation can be boosted to 95.61% under previously unseen RF data instances.

Original languageEnglish
Title of host publication2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages115-120
Number of pages6
ISBN (Electronic)9781665461290
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022 - Paris, France
Duration: 2 Nov 20223 Nov 2022

Publication series

NameIEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
Volume2022-November
ISSN (Electronic)2378-4873

Conference

Conference27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022
Country/TerritoryFrance
CityParis
Period2/11/223/11/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • data augmentation
  • Deep learning
  • radio frequency fingerprinting
  • secure design
  • unmanned aerial vehicles

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