do you have any sources for that?
I've only seen this mentioned from research results recently but no real world exploitation reports.
https://www.bleepingcomputer.com/news/security/new-acoustic-...
I got to 90% accuracy extremely quickly. The "guessed" keystrokes had errors but they were close enough to tell exactly what I was typing.
If I could do that as an amateur in a few hours of coding with no advanced signal processing and with the first SVM architecture I tried, it must be relatively easy to learn / classify.
From that discussion it sounds like you need to train on data captured from the actual target. Same physical keyboard in the same physical space with the same typer.
Pretty wild despite those specific conditions. Very interested to know if people have actually been attacked in the wild with this and if the attackers were able to generalize it down to just make and model of a keyboard, or if they could gather enough data from a stream.
It's quite good at decoding my own typing, although I am a quite aggressive typist and that may help. I haven't tried it on others, though (honest, officer).
For example, since users tend to type their passwords quicker than other things, you could see how many keystrokes were sent in a burst and guess the user's password length when they sudo something.
http://www.cs.berkeley.edu/~dawnsong/papers/ssh-timing.pdf [2001]
The addition of ML has greatly improved the accuracy of audio decoding - use a silent keyboard in any insecure physical locale.
https://arstechnica.com/gadgets/2023/08/type-softly-research...
https://www.usenix.org/legacy/events/sec01/full_papers/song/...
In this specific paper's use case, it's not a security threat, but you can definitely cast it as information leakage.