We present RFTrack, a new indoor location inference attack on Wi-Fi devices. This attack differs from existing Wi-Fi localization methods as it does not need bulky appliance deployment or inner physical access to the place of interest. RFTrack distinguishes itself by leveraging the temporal sequence of unlabeled Received Signal Strength Indicator (RSSI) values to deduce location labels. To achieve this, we deploy a Reinforcement Learning (RL) agent to model the most likely path of device movement and utilize these modeled trajectories to construct an RSSI fingerprint map. To enhance the accuracy of trajectory reconstruction, our technique exploits certain stationary Wi-Fi devices within the target area as reference points, facilitating the assessment of whether the mobile devices have traversed near specific zones with a newly proposed metric, the RSSI difference. The experimental results demonstrate that our system can accurately recover the trends of moving trajectories and successfully associate the unlabeled RSSI values with positions inside the place of interest to build a fingerprint map for real-time device tracking.

中文翻译：

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RFTrack：对 Wi-Fi 设备的隐秘位置推断和跟踪攻击


我们推出了 RFTrack，这是一种针对 Wi-Fi 设备的新型室内位置推断攻击。这种攻击与现有的 Wi-Fi 定位方法不同，因为它不需要部署庞大的设备或对感兴趣的地方进行内部物理访问。 RFTrack 的独特之处在于利用未标记的接收信号强度指示器 (RSSI) 值的时间序列来推断位置标签。为了实现这一目标，我们部署了强化学习 (RL) 代理来对最可能的设备移动路径进行建模，并利用这些建模轨迹来构建 RSSI 指纹图。为了提高轨迹重建的准确性，我们的技术利用目标区域内的某些固定 Wi-Fi 设备作为参考点，方便使用新提出的指标 RSSI 差值来评估移动设备是否已穿过特定区域。实验结果表明，我们的系统可以准确地恢复移动轨迹的趋势，并成功地将未标记的 RSSI 值与感兴趣地​​点内的位置相关联，以构建用于实时设备跟踪的指纹图。