Keystroke dynamics features for gender recognition

This work attempts to recognize the gender of an unknown user with data derived only from keystroke dynamics. Keystroke dynamics, which can be described as the way a user is typing, usually amount to tens of thousands of features, each of them enclosing some information. The question that arises is which of these characteristics are most suitable for gender classification. To answer this question, a new dataset was created by recording users during the daily usage of their computer, the information gain of each keystroke dynamics feature was calculated, and five well-known classification models were used to test the feature sets. The results show that the gender of an unknown user can be identified with an accuracy of over 95% with only a few hundred features. This percentage, which is the highest found in the literature, is quite promising for the development of reliable systems that can alert an unsuspecting user to being a victim of deception. Moreover, having the ability to identify the gender of a user who types a certain piece of text is of significant importance in digital forensics. This holds true, as it could be the source of circumstantial evidence for “putting fingers on the keyboard” and for arbitrating cases where the true origin of a message needs to be identified.