An approach to classify keystroke patterns for remote user authentication

The authentication of users is of utmost importance in remote applications such as healthcare, banking, stock markets, etc. Key stroke dynamics are popular biometrics tools used for this purpose. Continuous authentication requires free text analysis which has a number of challenges. This paper has proposed a solution to identify the existence of a unique pattern in each individual user’s keystroke dynamics. However, dense zone identification is important factor in forming the intelligent database of user profile for authentication. The authors have categorized basic key stroke features of digraph into 57 groups depending on distance traversed while moving from one key to another. The paper also includes graphical plots of the grouping of time vector which has unveiled some characteristics of overlapping typing style of users. The authors hope to extend this logic for identifying behavioral disorders in users.