Learning User Profile from Traces

Abstract

This paper presents a method for automatically constructing a sophisticated user/process profile from traces of user/process behavior. User profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM). The proposed method is based is on a recent algorithm, which is able to synthesize the HHMM structurefrom a set of logs of the user activity. The algorithm follows a bottom-up strategy, in which elementary facts in the sequences (motives) are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. The method is firstly evaluated on artificial data. Thena user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.