Adaptive Biometric Strategy using Doddington Zoo Classification of User’s Keystroke Dynamics

Securing personal, professional and even official data is a very critical issue nowadays, giving that these informations are safeguarded in different devices (mobile, computer) and various accounts (social networks, e-mails). To protect them from unauthorized access, users generally are asked to use passwords. But using only this authentication solution is no longer efficient against hacker attacks. Keystroke dynamics is a biometric promising modality that guarantees the recognition of the user's characteristics; his typing manner on the keyboard. Regarding that the typing rhythm of the user changes over time, adaptive biometric strategies help to take into consideration these variations during the authentication system. In this paper we classify users into multiple categories according to Doddington Zoo classification. Afterwards, we apply an adaptive strategy specific to each category of users. The achieved experiments demonstrate that an update strategy specific to the user class significantly improves the obtained performances.