Abstract
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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bolton, R.J., David, J.: Hand Statistical. Statistical fraud detection: A review. Statistical Science 17(3), 235–255 (2002)
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)
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)
Cortes, C., Pregibon, D., Volinsky, C.: Communities of interest. Intelligence Data Analysis 6(3), 211–219 (2002)
Cortes, C., Pregibon, D.: Signature-based methods for data streams. Data Mining and Knowledge Discovery (5), 167–182 (2001)
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)
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)
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)
Fawcett, T., Provost, F.: Adaptative fraud detection. In: Data Mining and Knowledge Discovery, pp. 1–28 (1997)
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22(2), 85–126 (2004)
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)
Leckie, T., Yasinsac, A.: Metadata for anomaly-based security protocol attack deduction. IEEE Trans. Knowl. Data Eng. 16(9), 1157–1168 (2004)
Lunt, T.F.: A survey of intrusion detection techniques. Computer and Security (53), 405–418 (1999)
McCarthy, J.: Phenomenal data mining. Commun. ACM 43(8), 75–79 (2000)
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)
Myers, Myers: Probability and Statistics for Engineers and Scientists, 6th edn. Prentice Hall, Englewood Cliffs
Pedrosa, A., Gama, S.: Introdução Computacional a Probabilidade e Estatistica, Porto Editora (2004)
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)
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)
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)
Weiss, G.M.: Data Mining in Telecommunications. Kluwer, Dordrecht (2004)
Weisstein, E.W.: Poisson distribution. From MathWorld–A Wolfram Web Resource (2006), http://mathworld.wolfram.com/PoissonDistribution.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferreira, P., Alves, R., Belo, O., Cortesão, L. (2006). Establishing Fraud Detection Patterns Based on Signatures. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_41
Download citation
DOI: https://doi.org/10.1007/11790853_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
Online ISBN: 978-3-540-36037-7
eBook Packages: Computer ScienceComputer Science (R0)