Deep Learning Methods for Device Identification Using Symbols Trace Plot
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911774483152896 |
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| author | Huang, Da Al-Hourani, Akram Sithamparanathan, Kandeepan Rowe, Wayne S. T. |
| author_facet | Huang, Da Al-Hourani, Akram Sithamparanathan, Kandeepan Rowe, Wayne S. T. |
| contents | Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) fingerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF fingerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identifiable fingerprints. By considering IQ imbalance solely as the feature source, DTP can efficiently extract device-identifiable fingerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classifiers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is verified using a practical dataset collected from the ADALM-PLUTO software-defined radios (SDRs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16622 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Learning Methods for Device Identification Using Symbols Trace Plot Huang, Da Al-Hourani, Akram Sithamparanathan, Kandeepan Rowe, Wayne S. T. Signal Processing Image and Video Processing Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) fingerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF fingerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identifiable fingerprints. By considering IQ imbalance solely as the feature source, DTP can efficiently extract device-identifiable fingerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classifiers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is verified using a practical dataset collected from the ADALM-PLUTO software-defined radios (SDRs). |
| title | Deep Learning Methods for Device Identification Using Symbols Trace Plot |
| topic | Signal Processing Image and Video Processing |
| url | https://arxiv.org/abs/2306.16622 |