Skip to main content
Log in

Bootstrapping trust of Web services based on trust patterns and Hidden Markov Models

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

We propose in this paper a new approach for bootstrapping trust of Web services in which the interactions of a Web service with a user are observed during a certain time frame. The observations sequence is modeled as a Hidden Markov Model and matched against pre-defined trust patterns in order to assess the behavior of such Web service. The pre-defined trust patterns are specifications of possible behaviors of Web services such as trusted, malicious, betraying, oscillating, and redemptive. Based on the matching result, an initial trust value is assigned to the Web service. Our experimental results show that our approach enjoys good precision and recall values and provides a fair distribution of trust values. Besides, the proposed approach is applied on a dataset of real-world Web services. A comparative study with published bootstrapping approaches shows a better bootstrapping success rate for our new approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. The training and testing sequence sets do not overlap in all the experiments carried out in this research.

  2. The sole exception is the recall measure for the trusted category.

  3. An entropy is considered low if it is smaller than a certain threshold. In this experiment, that threshold is set to 1.

  4. We use the average attribute value as the expected value just for the sake of this experiment. Ideally, the expected value of each attribute is taken from the service level agreement (SLA) of the corresponding service.

  5. We can also involve the values of \(p\) and \(q\) in the complexity analysis in which case the HMM construction will require the order of \(pqKN^4n^2\) computations.

References

  1. Plebani P, Pernici B (2009) URBE: Web service retrieval based on similarity evaluation. IEEE Trans Knowl Data Eng 21(11):1629–1642

    Article  Google Scholar 

  2. Sánchez D, Isern D, Millan M (2011) Content annotation for the semantic web: an automatic web-based approach. Knowl Inf Syst 27:393–418

    Article  Google Scholar 

  3. Chen C, Tseng F, Liang T (2011) An integration of fuzzy association rules and WordNet for document clustering. Knowl Inf Syst 28:687–708

    Article  Google Scholar 

  4. Yahyaoui H (2012) A trust-based game theoretical model for web services collaboration. Knowl Based Syst 27:162–169

    Article  Google Scholar 

  5. Xiong K, Perros H (2006) Trust-based Resource Allocation in Web Services. In: IEEE international conference on web services (ICWS’06) Chicago, Illinois, USA, pp 663–672

  6. Malik Z (2008) Reputation-based trust framework for service oriented environments. PhD thesis, Virginia Polytechnic Institute and State University

  7. Malik Z, Bouguettaya A (2009) RATEWeb: reputation assessment for trust establishment among web services. Very Large Data Bases (VLDB) 18(4):885–911

    Article  Google Scholar 

  8. Malik Z, Bouguettaya A (2009) Reputation bootstrapping for trust establishment among web services. IEEE Internet Comput 13(1):40–47

    Article  Google Scholar 

  9. Doshi P, Paradesi S, Swaika S (2009) Integrating behavioral trust in web service compositions. In: Proceedings of the seventh international conference on web services (ICWS’09). Los Angeles, CA, USA, pp 453–460

  10. Cao L, Yu P (eds) (2012) Behavior computing: modeling, analysis, mining and decision. Springer, Berlin

    Google Scholar 

  11. Cao L (2010) In-depth behavior understanding and use: the behavior informatics approach. Inf Sci 180(17):3067–3085

    Article  Google Scholar 

  12. Rabiner L (1989) A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  13. Rabiner L (1993) Fundamentals of speech recognition. Prentice Hall, Englewood Cliffs

    Google Scholar 

  14. Wright C, Monrose F, Masson G (2004) HMM profiles for network traffic classification. In: Proceedings of the ACM workshop on visualization and data mining for computer security. ACM, London, pp 9–15

  15. Maia J, Filho R (2010) Internet traffic classification using a Hidden Markov Model. In: Proceedings of the 10th international conference on hybrid intelligent systems (HIS), pp 37–42

  16. Krogh A, Larsson B, von Heijne G, Sonnhammer E (2001) Predicting transmembrane protein topology with a Hidden Markov Model: application to complete genomes. J Mol Biol 305:567–580

    Article  Google Scholar 

  17. Shliep A, Georgi B, Rungsaritytin W, Costa I, Schonhuth A (2004) In: Kremer K, Macho V (eds) The general Hidden Markov Model Library: analyzing systems with unobservable states, pp 121–136

  18. Yahyaoui H, Zhioua S (2011) Bootstrapping trust of web services through behavior observation. In: Proceedings of the 11th international conference on web engineering (ICWE 2011), pp 319–330

  19. Baum LE, Egon JA (1967) An inequality with applications to statistical estimation for probabilitsic functions of a Markov process and to a model of ecology. Bull Am Meteorol Soc 73:360–363

    Article  MATH  Google Scholar 

  20. Marti S, Garcia-Molina H (2006) Taxonomy of trust: categorizing P2P reputation systems. Comput Netw 50(4):472–484

    Article  MATH  Google Scholar 

  21. Moukas A, Zacharia G, Maes P (2000) Collaborative reputation mechanisms in electronic marketplaces. Decis Supp Syst 29(4):371–388

    Article  Google Scholar 

  22. Shannon C (1948) The mathematical theory of communication. Bell Syst Tech J 27:379–423

    Article  MathSciNet  MATH  Google Scholar 

  23. Chuong D, Batzogloul S (2008) What is the expectation maximization algorithm? Nat Biotechnol 26:897–899

    Article  Google Scholar 

  24. Murphy K (2005) Hidden Markov Model (HMM) Toolbox for Matlab. http://www.cs.ubc.ca/murphyk/Software/HMM/hmm.html

  25. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the international joint conference on artificial intelligence (IJCAI), pp 137–1143

  26. Refaeilzadeh P, Tang L, Liu H (2009) Cross validation. Encyclopedia of database systems. Springer, Berlin, pp 532–538

  27. Al-Masri E, Mahmoud Q (2007) Discovering the best web service. In: Proceedings of the 16th international world wide web conference, Banff, Canada, pp 1257–1258

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hamdi Yahyaoui.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yahyaoui, H., Zhioua, S. Bootstrapping trust of Web services based on trust patterns and Hidden Markov Models. Knowl Inf Syst 37, 389–416 (2013). https://doi.org/10.1007/s10115-012-0554-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-012-0554-1

Keywords

Navigation