  We present a new machine learning-based attack that exploits network patterns
to detect the presence of smart IoT devices and running services in the WiFi
radio spectrum. We perform an extensive measurement campaign of data
collection, and we build up a model describing the traffic patterns
characterizing three popular IoT smart home devices, i.e., Google Nest Mini,
Amazon Echo, and Amazon Echo Dot. We prove that it is possible to detect and
identify with overwhelming probability their presence and the services running
by the aforementioned devices in a crowded WiFi scenario. This work proves that
standard encryption techniques alone are not sufficient to protect the privacy
of the end-user, since the network traffic itself exposes the presence of both
the device and the associated service. While more work is required to prevent
non-trusted third parties to detect and identify the user's devices, we
introduce Eclipse, a technique to mitigate these types of attacks, which
reshapes the traffic making the identification of the devices and the
associated services similar to the random classification baseline.
