MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909524170899456 |
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| author | Mazokha, Stepan Bao, Fanchen Sklivanitis, George Hallstrom, Jason O. |
| author_facet | Mazokha, Stepan Bao, Fanchen Sklivanitis, George Hallstrom, Jason O. |
| contents | WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traffic to emitting devices without the use of MAC addresses.
We present MobRFFI, an AI-based device fingerprinting and re-identification framework for WiFi networks that leverages an encoder deep learning model to extract unique features based on WiFi chipset hardware impairments. It is entirely independent of frame type. When evaluated on the WiFi fingerprinting dataset WiSig, our approach achieves 94% and 100% device accuracy in multi-day and single-day re-identification scenarios, respectively.
We also collect a novel dataset, MobRFFI, for granular multi-receiver WiFi device fingerprinting evaluation. Using the dataset, we demonstrate that the combination of fingerprints from multiple receivers boosts re-identification performance from 81% to 100% on a single-day scenario and from 41% to 100% on a multi-day scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence Mazokha, Stepan Bao, Fanchen Sklivanitis, George Hallstrom, Jason O. Signal Processing Artificial Intelligence Machine Learning Networking and Internet Architecture WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traffic to emitting devices without the use of MAC addresses. We present MobRFFI, an AI-based device fingerprinting and re-identification framework for WiFi networks that leverages an encoder deep learning model to extract unique features based on WiFi chipset hardware impairments. It is entirely independent of frame type. When evaluated on the WiFi fingerprinting dataset WiSig, our approach achieves 94% and 100% device accuracy in multi-day and single-day re-identification scenarios, respectively. We also collect a novel dataset, MobRFFI, for granular multi-receiver WiFi device fingerprinting evaluation. Using the dataset, we demonstrate that the combination of fingerprints from multiple receivers boosts re-identification performance from 81% to 100% on a single-day scenario and from 41% to 100% on a multi-day scenario. |
| title | MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence |
| topic | Signal Processing Artificial Intelligence Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.02156 |