Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.07770 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908668029566976 |
|---|---|
| author | Guo, Zewei Jia, Zhen Zhu, JinXiao Huang, Wenhao Chen, Yin |
| author_facet | Guo, Zewei Jia, Zhen Zhu, JinXiao Huang, Wenhao Chen, Yin |
| contents | Radio frequency (RF) fingerprinting exploits hardware imperfections for device identification, but distinguishing between same-model devices remains challenging due to their minimal hardware variations. Existing datasets for RF fingerprinting are constrained by small device scales and heterogeneous models, which hinder robust training and fair evaluation of machine learning methods. To address this gap, we introduce a large-scale dataset of same-model devices along with an open-source experimental framework. The dataset is built using 123 same-model commercial IEEE 802.11g devices, which contain 35.42 million raw I/Q samples from the preambles and corresponding 1.85 million RF features. The accompanying framework further provides a fully reproducible pipeline from data collection to performance evaluation. Within this framework, a Random Forest-based algorithm is implemented as a baseline to achieve 89.06% identification accuracy on this dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SMoRFFI: A Large-Scale Same-Model 2.4 GHz Wi-Fi Dataset and Reproducible Framework for RF Fingerprinting Guo, Zewei Jia, Zhen Zhu, JinXiao Huang, Wenhao Chen, Yin Networking and Internet Architecture Radio frequency (RF) fingerprinting exploits hardware imperfections for device identification, but distinguishing between same-model devices remains challenging due to their minimal hardware variations. Existing datasets for RF fingerprinting are constrained by small device scales and heterogeneous models, which hinder robust training and fair evaluation of machine learning methods. To address this gap, we introduce a large-scale dataset of same-model devices along with an open-source experimental framework. The dataset is built using 123 same-model commercial IEEE 802.11g devices, which contain 35.42 million raw I/Q samples from the preambles and corresponding 1.85 million RF features. The accompanying framework further provides a fully reproducible pipeline from data collection to performance evaluation. Within this framework, a Random Forest-based algorithm is implemented as a baseline to achieve 89.06% identification accuracy on this dataset. |
| title | SMoRFFI: A Large-Scale Same-Model 2.4 GHz Wi-Fi Dataset and Reproducible Framework for RF Fingerprinting |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.07770 |