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 language | English |
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Title of host publication | 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 115-120 |
Number of pages | 6 |
ISBN (Electronic) | 9781665461290 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022 - Paris, France Duration: 2 Nov 2022 → 3 Nov 2022 |
Publication series
Name | IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD |
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Volume | 2022-November |
ISSN (Electronic) | 2378-4873 |
Conference
Conference | 27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022 |
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Country/Territory | France |
City | Paris |
Period | 2/11/22 → 3/11/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- data augmentation
- Deep learning
- radio frequency fingerprinting
- secure design
- unmanned aerial vehicles