Achieving all of that required the researchers to overcome two challenges. The first is that most assistant traffic is encrypted. That prevents LeakyPick from inspecting packet payloads to detect audio codecs or other signs of audio data. Second, with new, previously unseen voice assistants coming out all the time, LeakyPick also has to detect audio streams from devices without prior training for each device. Previous approaches, including one called HomeSnitch, required advanced training for each device model.
To clear the hurdles, LeakyPick periodically transmits audio in a room and monitors the resulting network traffic from connected devices. By temporarily correlating the audio probes with observed characteristics of the network traffic that follows, LeakyPick enumerates connected devices that are likely to transmit audio. One way the device identified likely audio transmissions is by looking for sudden bursts of outgoing traffic. Voice-activated devices typically send limited amounts of data when inactive. A sudden surge usually indicates a device has been activated and is sending audio over the Internet.
Using bursts alone is prone to false positives. To weed them out, LeakyPick employs a statistical approach based on an independent two-sample t-test to compare features of a device's network traffic when idle and when it responds to audio probes. This method has the added benefit of working on devices the researchers have never analyzed. The method also allows LeakyPick to work not only for voice assistants that use wake words, but also for security cameras and other Internet-of-things devices that transmit audio without wake words.
Guarding Against Accidental and Malicious Leaks
So far, LeakyPick—wh ..
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