Deep Adversarial Learning on Google Home devices
Cryptography and Security
2021-02-26 v1
Abstract
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.
Cite
@article{arxiv.2102.13023,
title = {Deep Adversarial Learning on Google Home devices},
author = {Andrea Ranieri and Davide Caputo and Luca Verderame and Alessio Merlo and Luca Caviglione},
journal= {arXiv preprint arXiv:2102.13023},
year = {2021}
}