Optic Fingerprint(OFP): Enhancing Security in Li-Fi Networks
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913798138363904 |
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| author | Liu, Ziqi Chen, Xuanbang Zhang, Xun |
| author_facet | Liu, Ziqi Chen, Xuanbang Zhang, Xun |
| contents | We present a hardware-integrated security framework for LiFi networks through device fingerprint extraction within the IEEE 802.15.7 protocol. Our Optic Fingerprint (OFP) model utilizes inherent LED nonlinearities to generate amplitude-based feature vectors in time and frequency domains, specifically designed for optical wireless systems. Experimental results with 39 commercial LEDs demonstrate 90.36% classification accuracy across SNR 10-30 dB while maintaining standard compliance, offering a practical physical-layer authentication solution for visible light communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optic Fingerprint(OFP): Enhancing Security in Li-Fi Networks Liu, Ziqi Chen, Xuanbang Zhang, Xun Signal Processing We present a hardware-integrated security framework for LiFi networks through device fingerprint extraction within the IEEE 802.15.7 protocol. Our Optic Fingerprint (OFP) model utilizes inherent LED nonlinearities to generate amplitude-based feature vectors in time and frequency domains, specifically designed for optical wireless systems. Experimental results with 39 commercial LEDs demonstrate 90.36% classification accuracy across SNR 10-30 dB while maintaining standard compliance, offering a practical physical-layer authentication solution for visible light communication. |
| title | Optic Fingerprint(OFP): Enhancing Security in Li-Fi Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.12956 |