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This paper presents an anomaly detector for keystroke dynamics authentication, based on a statistical measure of proximity, evaluated through the empirical study of an independent benchmark of keystroke data. A password typing-rhythm classifier is presented, to be used as an anomaly detector in the authentication process of genuine users and impostors. The proposed user authentication method involves two phases. First a training phase in which a user typing profile is created through repeated entry of password. In the testing phase, the password typing rhythm of the user is compared with the stored typing profile, to determine whether it is a genuine user or an impostor. The typing rhythm is obtained through keystroke timings of key-down/key-up of individual keys and the latency between keys.
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