Hierarchical Hidden Markov Models for User/Process Profile Learning

Issue title: Special issue ISMIS'05

Article type: Research Article

Authors: Galassi, Ugo | Botta, Marco | Giordana, Attilio

Affiliations: Dipartimento di Informatica, Università Amedeo Avogadro, Via Bellini 25G, 15100, Alessandria, Italy. E-mail: info@ugogalassi.net; attilio@mfn.unipmn.it | Dipartimento di Informatica, Università di Torino, C.so Svizzera 185, 10149 Torino, Italy. E-mail: marco.botta@di.unito.it

Abstract: This paper presents an algorithmfor automatically constructing sophisticated user/process profiles from traces of their behavior. A profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM), which is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. A special sub-class of this hierarchical model, oriented to user/process profiling, is also introduced. The algorithm follows a bottom-up strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. The method is firstly evaluated on artificial data. Then a user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.

Journal: Fundamenta Informaticae, vol. 78, no. 4, pp. 487-505, 2007