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The increasing ubiquity of low-cost wireless sensors has enabled users toeasily deploy systems to remotely monitor and control their environments.However, this raises privacy concerns for third-party occupants, such as ahotel room guest who may be unaware of deployed clandestine sensors. Previousmethods focused on specific modalities such as detecting cameras but do notprovide a generalized and comprehensive method to capture arbitrary sensorswhich may be "spying" on a user. In this work, we propose SnoopDog, a frameworkto not only detect common Wi-Fi-based wireless sensors that are activelymonitoring a user, but also classify and localize each device. SnoopDog worksby establishing causality between patterns in observable wireless traffic and atrusted sensor in the same space, e.g., an inertial measurement unit (IMU) thatcaptures a user's movement. Once causality is established, SnoopDog performspacket inspection to inform the user about the monitoring device. Finally,SnoopDog localizes the clandestine device in a 2D plane using a noveltrial-based localization technique. We evaluated SnoopDog across severaldevices and various modalities and were able to detect causality for snoopingdevices 95.2% of the time and localize devices to a sufficiently reducedsub-space.

I Always Feel Like Somebody's Sensing Me! A Framework to Detect, Identify, and Localize Clandestine Wireless Sensors