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We present a new machine learning-based attack that exploits network patternsto detect the presence of smart IoT devices and running services in the WiFiradio spectrum. We perform an extensive measurement campaign of datacollection, and we build up a model describing the traffic patternscharacterizing 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 andidentify with overwhelming probability their presence and the services runningby the aforementioned devices in a crowded WiFi scenario. This work proves thatstandard encryption techniques alone are not sufficient to protect the privacyof the end-user, since the network traffic itself exposes the presence of boththe device and the associated service. While more work is required to preventnon-trusted third parties to detect and identify the user's devices, weintroduce Eclipse, a technique to mitigate these types of attacks, whichreshapes the traffic making the identification of the devices and theassociated services similar to the random classification baseline.

The Dark (and Bright) Side of IoT: Attacks and Countermeasures for Identifying Smart Home Devices and Services