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Smart speakers and voice-based virtual assistants are core components for thesuccess of the IoT paradigm. Unfortunately, they are vulnerable to variousprivacy threats exploiting machine learning to analyze the generated encryptedtraffic. To cope with that, deep adversarial learning approaches can be used tobuild black-box countermeasures altering the network traffic (e.g., via packetpadding) and its statistical information. This letter showcases the inadequacyof such countermeasures against machine learning attacks with a dedicatedexperimental campaign on a real network dataset. Results indicate the need fora major re-engineering to guarantee the suitable protection of commerciallyavailable smart speakers.

Deep Adversarial Learning on Google Home devices