用硬件指纹识别随机MAC的设备，提升城市移动监测精度
MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence
- 基于WiFi芯片硬件缺陷提取唯一特征，不依赖帧类型
- 单日重识别准确率100%，多日达94%
- 多接收机融合指纹可将准确率从41%提升至100%
基于WiFi的城市移动监测可提供行人与车辆轨迹的宝贵信息。然而，MAC地址随机化严重阻碍了拥堵水平和路径轨迹的准确估计。为此，本文提出一种无需使用MAC地址的射频指纹与设备重识别方法。我们设计了MobRFFI，一个基于深度学习编码器的WiFi设备指纹与重识别框架，利用芯片硬件缺陷提取唯一特征，完全独立于帧类型。在WiSig数据集上评估，该方法在单日和多日重识别场景中分别达到100%和94%的设备识别准确率。此外，我们构建了新的数据集MobRFFI，用于细粒度多接收机场景下的评估。实验表明，多个接收机指纹融合可将单日场景准确率从81%提升至100%，多日场景从41%提升至100%。
原文摘要 · Abstract (English)
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.
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