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2007

Modeling Temporal Behavior via Structured Hidden Markov Models: an Application to Keystroking Dynamics

11 years 3 months ago
Modeling Temporal Behavior via Structured Hidden Markov Models: an Application to Keystroking Dynamics
Structured Hidden Markov Models (S-HMM) are a variant of Hierarchical Hidden Markov Models; it provides an abstraction mechanism allowing a high level symbolic description of the knowledge embedded in S-HMM to be easily obtained, at the same time reducing the complexity of using and learning the model. S-HMMs are particularly well suited to build up profiles of discrete processes, described by meaningful sequences of symbols possibly interleaved with gaps, i.e., subsequences whose useful information resides in their duration and not in their content. In this paper we will first introduce the model, and then we will concentrate on the description of an application, namely the characterization of biometric sequences (keyboard stroke duration) used for an identification task in computer access. Key words: Hidden Markov Model, keystroking dynamics, user authentication.
Ugo Galassi, Attilio Giordana, Charbel Julien, Lor
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IICAI
Authors Ugo Galassi, Attilio Giordana, Charbel Julien, Lorenza Saitta
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