Keystroke anomaly detector

design and implement a basic keystroke anomaly detector using either

Euclidean distance or Manhattan distance. Your detector will take 10 keystroke login samples per

user and generate keystroke signature for this user. When a new keystroke login sample for user X is

tested by the detector against user X keystroke signature the detector will return a value between

[0-1]. Where 0 indicate no match, 1 indicate a perfect match and any value above 0.85 is considered

a match

Taidot: Keinoäly, Python

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Hi, I have 5+years Experience in Website/Software Design & Website Development. I'm an Expert in: -Frontend & Backend Development -Python -Data Science -Machine Learning -JVM technologies -Jquery, PHP -Ion Lisää

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