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Learning Profiles Based on Hierarchical Hidden Markov Model

  • Ugo Galassi
  • Attilio Giordana
  • Lorenza Saitta
  • Maco Botta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)

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 HHMM is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. The method described here is based on a recent algorithm, which is able to synthesize the HHMM structure from 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. Then a user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.

Keywords

Speech Recognition Viterbi Algorithm Average Error Rate Real Trace Abstraction Hierarchy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bleha, S., Slivinsky, C., Hussein, B.: Computer-access security systems using keystroke dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-12(12), 1217–1222 (1990)CrossRefGoogle Scholar
  2. 2.
    Botta, M., Galassi, U., Giordana, A.: Learning complex and sparse events in long sequences. In: Proceedings of the European Conference on Artificial Intelligence, ECAI 2004, Valencia, Spain (August 2004)Google Scholar
  3. 3.
    Brown, M., Rogers, S.J.: User identification via keystroke characteristics of typed names using neural networks. International Journal of Man-Machine Studies 39, 999–1014 (1993)CrossRefGoogle Scholar
  4. 4.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis. Cambridge University Press, Cambridge (1998)zbMATHCrossRefGoogle Scholar
  5. 5.
    Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)zbMATHCrossRefGoogle Scholar
  6. 6.
    Forney, G.D.: The viterbi algorithm. Proceedings of IEEE 61, 268–278 (1973)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Gussfield, D.: Algorithms on Strings, Trees, and Sequences. Cambridge University Press, Cambridge (1997)CrossRefGoogle Scholar
  8. 8.
    Joyce, R., Gupta, G.: User authorization based on keystroke latencies. Communications of the ACM 33(2), 168–176 (1990)CrossRefGoogle Scholar
  9. 9.
    Murphy, K., Paskin, M.: Linear time inference in hierarchical hmms. In: Advances in Neural Information Processing Systems (NIPS 2001), vol. 14 (2001)Google Scholar
  10. 10.
    Rabiner, L., Juang, B.: Fundamentals of Speech Recognition. Prentice Hall, Englewood Cliffs (1993)Google Scholar
  11. 11.
    Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ugo Galassi
    • 1
  • Attilio Giordana
    • 1
  • Lorenza Saitta
    • 1
  • Maco Botta
    • 2
  1. 1.Dipartimento di InformaticaUniversità Amedeo AvogadroAlessandriaItaly
  2. 2.Dipartimento di InformaticaUniversitá di TorinoTorinoItaly

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