Behavioral Biometrics for User Authentication Using Self-Refreshing SOM

Password protection has been a crucial issue in computer security. Traditional password protection using string does not provide sufficient security for user authentication. On the other hand, implementation of static biometrics in replace of password protection is costly as specific devices are needed. This research proposes the use of Self-Organizing Map (SOM) method for keystroke pattern recognition as an alternative to password protection. A self-refreshing SOM method was also implemented to increase the adaptability of SOM toward inconsistencies in typing pattern. Two main experiments were conducted to examine the ability of SOM for user recognition and the ability of self-refreshing SOM to deal with typing pattern inconsistencies. The first experiment shows the ability of SOM to authenticate users with very accurate classification. Users were successfully recognized and non-users were successfully rejected. Second experiment shows the ability of self-refreshing SOM to deal with inconsistencies in typing pattern of a same user in which smoother transition of data trend was obtained.