Completed

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

Skills: Artificial Intelligence, Python

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