RFTrack: Stealthy Location Inference and Tracking Attack on Wi-Fi Devices

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.