Abstract
Telecommunications fraud has drawn the attention in research due to the huge economic burden on companies and to the interesting aspect of users’ behavior characterization. In the present paper, we deal with the issue of user characterization. Several real cases of defrauded user accounts for different user profiles were studied. Each profile’s ability to characterize user behavior in order to discriminate normal activity from fraudulent one was tested. Feed-forward neural networks were used as classifiers. It is found that summary characteristics of user’s behavior perform better than detailed ones towards this task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gosset, P., Hyland, M.: Classification, detection and prosecution of fraud in mobile networks. In: Proceedings of ACTS Mobile Summit, Sorrento Italy (1999)
Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Statistical Science 17(3), 235–255 (2002)
ACTS ACo95, project ASPeCT: Legal aspects of fraud detection. AC095/KUL/W26/ DS/P/25/2 (1998)
Hilas, C.S., Sahalos, J.N.: User profiling for fraud detection in telecommunications networks. In: Proc. of the 5th Int. Conf. on Technology and Automation - ICTA 2005, Thessaloniki Greece, pp. 382–387 (2005)
Moreau, Y., Preneel, B., Burge, P., Shawe-Taylor, J., Stoermann, C., Cooke, C.: Novel Techniques for Fraud Detection in Mobile Telecommunication Networks. ACTS Mobile Summit. Granada Spain (1997)
Buschkes, R., Kesdogan, D., Reichl, P.: How to increase security in Mobile Networks by Anomaly Detection. In: Proc. of the 14th Annual Computer Security Applications Conference - ACSAC 1998, vol. 8 (1998)
Rosset, S., Murad, U., Neumann, E., Idan, Y., Pinkas, G.: Discovery of fraud rules for telecommunications – challenges and solutions. In: Proc. of ACM SIGKDD 1999, pp. 409–413. ACM Press, San Diego (1999)
Adomavicious, G., Tuzhilin, A.l.: User profiling in Personalization Applications through Rule Discovery and Validation. In: Proc. of ACM SIGKDD 1999, pp. 377–381. ACM Press, San Diego (1999)
Fawcett, T., Provost, F.: Adaptive Fraud Detection. Data Mining and Knowledge Discovery 1, 291–316 (1997)
Oh, S.H., Lee, W.S.: An anomaly intrusion detection method by clustering normal user behavior. Computers & Security 22(7), 596–612 (2003)
Cox, K.C., Eick, S.G., Wills, G.J., Brachman, R.J.: Visual data mining: Recognizing telephone calling fraud. J. Data Mining and Knowledge Discover 1(2), 225–231 (1997)
Taniguchi, M., Haft, M., Hollmen, J., Tresp, V.: Fraud detection in communication networks using neural and probabilistic methods. In: Proc. of the 1998 IEEE Int. Conf. Acoustics, Speech and Signal Processing, vol. 2, pp. 1241–1244 (1998)
Manikopoulos, C., Papavassilliou, S.: Network Intrusion and Fault Detection: A Statistical Anomaly Approach. IEEE Communications Magazine, Vol 40(10), 76–82 (2002)
Hollmen, J., Tresp, V.: Call-based fraud detection in mobile communication networks using a hierarchical regime switching model. In: Proc. of the 1998 Conf. on Advances in Neural Information Processing Systems II, pp. 889–895. MIT Press, Cambridge (1999)
Phua, C., Alahakoon, D., Lee, V.: Minority Report in Fraud Detection: Classification of Skewed Data. ACM SIGKDD Explorations – Special Issue on Imbalanced Data Sets 6(1), 50–58 (2004)
Fawcett, T., Phua, C.: Fraud Detection Bibliography (2005), Accessed from http://liinwww.ira.uka.de/bibliography/Ai/fraud.detection.html
Hinde, S.F.: Call Record Analysis. In: IEE Colloquium on Making Life Easier - Network Design and Management Tools (Digest No: 1996/217), pp. 8/1–8/4 (1996)
Manly, B.F.J.: Multivariate Statistical Methods: A Primer, 2nd edn. Chapman & Hall, Boca Raton (1994)
Lin, C.-T., Lee, G.C.S.: Neural-Fuzzy Systems: a neuro fuzzy synergism to intelligent systems. Prentice Hall, Upper Saddle River (1996)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: Proc. of the IEEE Int. Conf. on Neural Networks, vol. 1, pp. 586–591 (1993)
Downey, T.J., Meyer, D.J., Price, R.K., Spitznagel, E.L.: Using the receiver operating characteristic to asses the performance of neural classifiers. In: Int. Joint Conf. Neural Networks, vol. 5, pp. 3642–3646 (1999)
Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proc of the Fifteenth Int. Conf. on Machine Learning, pp. 43–48. Morgan Kaufmann, San Francisco (1998)
Kajitani, Y., Hipel, K.W., McLeod Ian, A.: Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data. Journal of Forecasting 24(2), 105–117 (2005)
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
Hilas, C.S., Sahalos, J.N. (2006). Testing the Fraud Detection Ability of Different User Profiles by Means of FF-NN Classifiers. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_91
Download citation
DOI: https://doi.org/10.1007/11840930_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)