  The increasing ubiquity of low-cost wireless sensors has enabled users to
easily deploy systems to remotely monitor and control their environments.
However, this raises privacy concerns for third-party occupants, such as a
hotel room guest who may be unaware of deployed clandestine sensors. Previous
methods focused on specific modalities such as detecting cameras but do not
provide a generalized and comprehensive method to capture arbitrary sensors
which may be "spying" on a user. In this work, we propose SnoopDog, a framework
to not only detect common Wi-Fi-based wireless sensors that are actively
monitoring a user, but also classify and localize each device. SnoopDog works
by establishing causality between patterns in observable wireless traffic and a
trusted sensor in the same space, e.g., an inertial measurement unit (IMU) that
captures a user's movement. Once causality is established, SnoopDog performs
packet inspection to inform the user about the monitoring device. Finally,
SnoopDog localizes the clandestine device in a 2D plane using a novel
trial-based localization technique. We evaluated SnoopDog across several
devices and various modalities and were able to detect causality for snooping
devices 95.2% of the time and localize devices to a sufficiently reduced
sub-space.
