4 ms·

I was a bit disappointed by the lack of a real scenario implementation. While the data suggests it might work, I wanted to see how precise it would be in a simp

I was a bit disappointed by the lack of a real scenario implementation. While the data suggests it might work, I wanted to see how precise it would be in a simple implementation like the author described towards the end, where a simple game of tapping trains the NN and then uses the information to try and capture a typed password. If I got that close to making something as cool as this, I would not stop experimenting until I found out whether it worked or not. That is, unless I tested it, found that the imprecisions made it impossible to obtain reliable data, and decided to not write about it to not discredit all the work I had done. I really hope someone takes the idea and tries it out to assess how viable it is, then I can be paranoid about 2 more sensors spying on me.

andrewguenther 9y ago
The Usenix paper they referenced from 2011 did exactly this. https://www.usenix.org/legacy/event/hotsec11/tech/final_files/Cai.pdf https://www.usenix.org/legacy/event/hotsec11/tech/final_file... They focused purely on the number keyboard to try and extract PIN numbers and were able to achieve 70% accuracy using only a basic classifier.
- Unknoob 9y ago
  Thank you for reminding me of the references, I had forgotten to check them out! I wonder how much newer smartphones with bigger screens and more precise sensors could improve these results. It's scary to think of how many different sensors could be used to gather even more information to make it more precise.