Abstract
One of the most severe threats to revenue and quality of service in telecom providers is fraud. The advent of new technologies has provided fraudsters new techniques to commit fraud. SIM box fraud is one of such fraud that has emerged with the use of VOIP technologies. In this work, a total of nine features found to be useful in identifying SIM box fraud subscriber are derived from the attributes of the Customer Database Record (CDR). Artificial Neural Networks (ANN) has shown promising solutions in classification problems due to their generalization capabilities. Therefore, supervised learning method was applied using Multi layer perceptron (MLP) as a classifier. Dataset obtained from real mobile communication company was used for the experiments. ANN had shown classification accuracy of 98.71 %.
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
References
Taniguchi M, Haft M, Hollmen J, Tresp V (1998) Fraud detection in communications networks using neural and probabilistic methods. In: Proceedings of the 1998 IEEE international conference on acoustics speech and signal processing, vol 2. IEEE, Los Alamitos, pp 1241–1244
Hilas C, Mastorocostas P (2008) An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowl Based Syst 21(7):721–726
Azgomi NL (2009) A taxonomy of frauds and fraud detection techniques. In: Proceedings of CISTM 2009, Ghaziabad, India, pp 256–267
Telenor GS (2010) Global SIM box detection
Nokia Siemens Neworks Corporation (2008). Battling illegal call operations with fraud management systems
Larose DT (2005) Discovering knowledge in data. John Wiley and Sons, Inc., Hoboken
Ghosh M (2010) Telecoms fraud. Comput Fraud Secur 2010(7):14–17
MacLennan J (2009) Data mining with Microsoft SQL Server 2008. Wiley Publishing Inc, Indianapolis
Mark EM, Venkayala S (2007) Java data mining strategy, standard, and practice. Diane Cerra, San Francisco
Cortesao L, Martins F, Rosa A, Carvalho P (2005) Fraud management systems in telecommunications: a practical approach. In: Proceeding of ICT, 2005
Pablo A, Este′vez CM, Claudio AP (2005) Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. In: Proceedings of the expert systems with applications. Santiago, Chile, 2005
Hilas C, Mastorocostas P (2008) An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowl Based Syst 21(7):721–726
Acknowledgments
The authors first thank the anonymous reviewers for their valuable comments and to Universiti Teknologi Malaysia (UTM) for the FRGS Grant Vote number 4F086 that is sponsored by Ministry of Higher Education (MOHE) and Research Management Centre, Universiti Teknologi Malaysia, Skudai, Johor.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Elmi, A.H., Ibrahim, S., Sallehuddin, R. (2013). Detecting SIM Box Fraud Using Neural Network. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_69
Download citation
DOI: https://doi.org/10.1007/978-94-007-5860-5_69
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5859-9
Online ISBN: 978-94-007-5860-5
eBook Packages: EngineeringEngineering (R0)