this post was submitted on 02 Dec 2023
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Because of different placement on the keyboard and different finger pressure, each key press has a slightly different sound.
The telling thing in this story is this
For some people (those with a very consistent typing style on a known keyboard) they were right 95% of the time.
In the real world this type of thing is basically useless as you would need a decent sample of the person typing on a known keyboard for it to work.
So to do this you need to have physical access to the person (to place a microphone nearby) and know what type of device they are typing on and for it to be a device that you have already analysed the sound profile of.
The article says
Hm. Sounds like "some cases" are hunt and peck typists or very slow touch typists.
I don't know if training for each victim's typing is really needed. I get the impression they were identifying unique sounds and converting that to the correct letters. I only skimmed and I didn't quite understand the description of the mechanisms. Something about deep learning and convolution or...? I think they also said they didn't use a language model so I could be wrong.
The problems is that even with up to 95% accuracy that still means the with a password length of 10 there is a 50/50 chance that one character is wrong.
A password with one character wrong is just as useless as randomly typing.
Which character is wrong and what should it be? You only have 2 or 3 more guess till most systems will lock the account.
This is an interesting academic exercise but there are much better and easier ways to gain access to passwords and systems.
The world is not a bond movie.
Deploying social engineering is much easier than this sort of attack.
"Hearing" the same password twice drastically increases the accuracy, however, social engineering is indeed the most effective and efficient attack method.
If the password is not random, as they seldomly are, you can just guess the last, or even the last few characters of they are not correct.
Have you never seen a Bond movie? Yeah they always have a gadget or two, but the rest is basically him social engineering his way through the film. And shooting. Usually lots of shooting too.
I was thinking of this attack in terms of grabbing emails, documents, stuff like that. Or snippets thereof.
I imagine it probably also uses an algorithm to attempt to "guess" the next letter (or the full word itself, like your phone keyboard does) based on existing words. Then maybe an LLM can determine which of the potential words are the most likely being typed based on the context.
I dunno if that makes any sense, but that's how I pictured it working in my brain movies.
You don’t need physical access, just some malware that has access to the microphone
We would hope researchers “discovering” this wouldn’t have a production ready product as their proof of concept. So there is room from improvement but military contractors would love to invest in this
Which you still need to have previously installed...
If the person has allowed malware to be installed just install a keylogger (which gives you 100% accuracy every time) rather than jump through more hoops with this.
Different devices
I would have an easier time infecting someone‘s personal phone than a company machine
You would, would you?
Well, I must be talking to a leet hacker then.
Ok, install malware on my phone.
How did you get that from what I said?
What did you mean by this then other than you, personally, are skilled at such things and have system penetration experience?
Easier doesn’t mean easy but I can send you an email/give you a link
The company email server should block it and the firewall should block the website
Sample
Check out this game! https://play.google.com/store/apps/details?id=com.robtopx.geometrydashsubzero
But the page is actually
https://play.giggle.com/store/apps/details?id=com.robtopx.geometrydashsubzero
Knowing this doesn’t make me 1337
They'll have modelled the acoustic signals to differentiate between different keys. Individual acoustic waves eminating from pressing a key will have features extracted from them to identify them. Opimal featues are then choose to maximise accuracy, such as features that still work when the signal is captured at different distances or angles. With all these types of singsl processing inference models, you never get 100 percent. The claim of 95 percent is actually very high.
Every key is unique and at a different distance to the microphone and therefore makes tiny differences in noise.
Knowing this, and knowing the frequency distribution of letters in language (e.g. we know "e" is the most common letter) and some clever analysis over a large enough sample of typing, we can figure out what each key sounds like with a statically high level of probability. Once that's happened it's just like any other speech recognition software, except it's the language of your keyboard.
This is just me kindof guessing off the top of my head, but:
Now, the researchers didn't sit down and list out all of these (or any other) ways in which software could determine what was typed from audio and compose an algorithm that accounted for all/most/some of these. They just kindof threw a bunch of audio with accompanying "right answers" at a machine learning algorithm and let the algorithm figure out whatever clues it could discern and combine those in whatever way it found most beneficial to come up with an (increasingly-more-accurate-with-every-training-set) answer. It's likely the algorithm came up with different things than I did that helped it determine which key(s) were being pressed.