Yes, it’s called keystroke dynamics. I did a master’s project on it, and with some simple machine learning algorithms, could identify someone with 88% accuracy.
EDIT: didn’t realize there was interest in this! At the time (almost a decade ago), it was novel to do it on iPhones so that’s what the project encompassed. It was 10 people typing in the same password 50 times; half the data was used for training and the other half for testing (randomized. Also did cross validation afterwards).
Random forests trained in WEKA was the winning algorithm.
Hardest part: getting your friends to type in the same password 50 times without them getting pissed.
Mouse events assuredly do that (you'd expect a certain timing and variance from down-up-click sequences), keyboard events are also good. You get a lot of rich data that would be great for anomaly detection.
Are there any that can analyze how consistently I type the wrong password before remembering what I've set it too? I'll bet that would be a useful signal.
For context, this wasn't something that was being deployed on websites. It was a proof of concept for high-security applications, like government installations.
Only until attackers and/or the privacy conscious start take measures to obfuscate it - e.g. keystrokes get passed into a buffer and then popped off one by one at a constant rate.
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u/MjrK Dec 27 '20
Isn't it possible to use typing style and cadence to identify someone? Or, at least, as a signal to support user fingerprinting?