  Smart speakers and voice-based virtual assistants are core components for the
success of the IoT paradigm. Unfortunately, they are vulnerable to various
privacy threats exploiting machine learning to analyze the generated encrypted
traffic. To cope with that, deep adversarial learning approaches can be used to
build black-box countermeasures altering the network traffic (e.g., via packet
padding) and its statistical information. This letter showcases the inadequacy
of such countermeasures against machine learning attacks with a dedicated
experimental campaign on a real network dataset. Results indicate the need for
a major re-engineering to guarantee the suitable protection of commercially
available smart speakers.
