Establishing Fraud Detection Patterns Based on Signatures

  • Pedro Ferreira
  • Ronnie Alves
  • Orlando Belo
  • Luís Cortesão
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)


All over the world we have been assisting to a significant increase of the telecommunication systems usage. People are faced day after day with strong marketing campaigns seeking their attention to new telecommunication products and services. Telecommunication companies struggle in a high competitive business arena. It seems that their efforts were well done, because customers are strongly adopting the new trends and use (and abuse) systematically communication services in their quotidian. Although fraud situations are rare, they are increasing and they correspond to a large amount of money that telecommunication companies lose every year. In this work, we studied the problem of fraud detection in telecommunication systems, especially the cases of superimposed fraud, providing an anomaly detection technique, supported by a signature schema. Our main goal is to detect deviate behaviors in useful time, giving better basis to fraud analysts to be more accurate in their decisions in the establishment of potential fraud situations.


Feature Variable User Behavior Anomaly Detection User Signature Customer Behavior 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bolton, R.J., David, J.: Hand Statistical. Statistical fraud detection: A review. Statistical Science 17(3), 235–255 (2002)MATHGoogle Scholar
  2. 2.
    Burge, P., Shawe-Taylor, J., Moreau, Y., Verrelst, H., Stoermann, C., Gosset, P.: Fraud detection and management in mobile telecommunications networks. In: Proceedings of the 2nd IEEE European Conference on Security and Detection, London, vol. 437, pp. 91–96. IEEE, Los Alamitos (1997)Google Scholar
  3. 3.
    Cahill, M., Lambert, D., Pinheiro, J., Sun, D.: Detecting fraud in the real world. In: Handbook of massive data sets, pp. 911–929. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  4. 4.
    Cortes, C., Pregibon, D., Volinsky, C.: Communities of interest. Intelligence Data Analysis 6(3), 211–219 (2002)MATHGoogle Scholar
  5. 5.
    Cortes, C., Pregibon, D.: Signature-based methods for data streams. Data Mining and Knowledge Discovery (5), 167–182 (2001)Google Scholar
  6. 6.
    Das, K., Moore, A., Schneider, J.: Belief state approaches to signaling alarms in surveillance systems. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 539–544. ACM Press, New York (2004)CrossRefGoogle Scholar
  7. 7.
    Fan, W.: Systematic data selection to mine concept-drifting data streams. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 128–137. ACM Press, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Fawcett, T., Provost, F.: Combining data mining and machine learning for effective user profiling. In: Simoudis, Han, Fayyad (eds.) Proceedings on the Second International Conference on Knowledge Discovery and Data Mining, pp. 8–13. AAAI Press, Menlo Park (1996)Google Scholar
  9. 9.
    Fawcett, T., Provost, F.: Adaptative fraud detection. In: Data Mining and Knowledge Discovery, pp. 1–28 (1997)Google Scholar
  10. 10.
    Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22(2), 85–126 (2004)CrossRefMATHGoogle Scholar
  11. 11.
    Kou, Y., Lu, T., Sirwongwattana, S., Huang, Y.: Survey of fraud detection techniques. In: Proceedings of 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan. IEEE, Los Alamitos (2004)Google Scholar
  12. 12.
    Leckie, T., Yasinsac, A.: Metadata for anomaly-based security protocol attack deduction. IEEE Trans. Knowl. Data Eng. 16(9), 1157–1168 (2004)CrossRefGoogle Scholar
  13. 13.
    Lunt, T.F.: A survey of intrusion detection techniques. Computer and Security (53), 405–418 (1999)Google Scholar
  14. 14.
    McCarthy, J.: Phenomenal data mining. Commun. ACM 43(8), 75–79 (2000)CrossRefGoogle Scholar
  15. 15.
    Moreau, Y., Verrelst, H., Vandewalle, J.: Detection of mobile phone fraud using supervised neural networks: A first prototype. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 1065–1070. Springer, Heidelberg (1997)Google Scholar
  16. 16.
    Myers, Myers: Probability and Statistics for Engineers and Scientists, 6th edn. Prentice Hall, Englewood CliffsGoogle Scholar
  17. 17.
    Pedrosa, A., Gama, S.: Introdução Computacional a Probabilidade e Estatistica, Porto Editora (2004)Google Scholar
  18. 18.
    Rosset, S., Murad, U., Neumann, E., Idan, Y., Pinkas, G.: Discovery of fraud rules for telecommunications challenges and solutions. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 409–413. ACM Press, New York (1999)CrossRefGoogle Scholar
  19. 19.
    Shawe-Taylor, J., Howker, K., Gosset, P., Hyland, M., Verrelst, H., Moreau, Y., Stoermann, C., Burge, P.: Novel techniques for profiling and fraud detection in mobile telecommunications. In: Business Applications of Neural Networks, pp. 113–139. World Scientific, Singapore (2000)CrossRefGoogle Scholar
  20. 20.
    Taniguchi, M., Haft, M., Hollmen, J., Tresp, V.: Fraud detection in communications networks using neural and probabilistic methods. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 1998), vol. 2, pp. 1241–1244 (1998)Google Scholar
  21. 21.
    Weiss, G.M.: Data Mining in Telecommunications. Kluwer, Dordrecht (2004)Google Scholar
  22. 22.
    Weisstein, E.W.: Poisson distribution. From MathWorld–A Wolfram Web Resource (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pedro Ferreira
    • 1
  • Ronnie Alves
    • 1
  • Orlando Belo
    • 1
  • Luís Cortesão
    • 2
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.Portugal Telecom InovaçãoAveiroPortugal

Personalised recommendations