Detecting SIM Box Fraud by Using Support Vector Machine and Artificial Neural Network
DOI:
https://doi.org/10.11113/jt.v74.2649Keywords:
SIM box fraud, artificial neural network, support vector machine, classification, accuracyAbstract
Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud.  The classification uses nine selected features of data extracted from Customer Database Record.  The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training.References
Zou K., Sum W., Yu H. and Liu F. 2012. ID3 Decision Tree in Fraud Detection Application. International Conference on Computer Science and Electronics Engineering. 399–402.
Azgomi N.L. 2009. A Taxonomy of Frauds and Fraud Detection Techniques. Proceeding of CISTM 2009 Ghaziabad: India. 256–267.
Hilas, C. and Sahalos, J. 2005. User Profiling for Fraud detection in Telecommunication Networks. 5th International Conference on Technology and Automation. 382–387.
Suman and Nutan. 2013. Review Paper on Credit Card Fraud Detection. International Journal of Computer Trends and Technology. 4(7): 2206–2215.
Bolton, R. J. and Hand, D. T. 2002. Statistical Fraud Detection: A Review. Statistical Science. 17: 235–249.
Telenor, G. S. 2010. Global SIM Box Detection.
Hynninen J. 2000. Experience in Mobile Phone Fraud.
Phual C., Lee V., Smith K. and Ross G. A. 2007. A Comprehensive Survey of Data Mining-based Fraud Detection Research. Artificial Intelligent Review.
Barson P., Field S., Davey N., McAskie G. and Frank R. 1996. The Detection of Fraud in Mobile Phone Networks. Neural Network World. 6(4): 477–484.
Krenker A., Volk, M., Sedlar, U., Bester, J. and Kos, A. 2009. Bidirectional Artificial Neural Networks for Mobile-phone Fraud Detection. Etri Journal. 31(1): 92–94.
Farvaresh, H. and Sepehri, M. M. 2011. A Data Mining Framework for Detecting Subscription Fraud in Telecommunication. Engineering Applications of Artificial Intelligence. 24(1): 182–194.
MacLennan, J. 2009. Data Mining with Microsoft SQL Server. 2008. Wiley Publishing Inc: Indianapolis.
Mark E.M. and Venkayala S. 2007. Java Data Mining Strategy, Standard, and Practice. San Francisco: Diane D.Cerra.
Paliwal M. and Kumar U.A. 2009. Neural Networks and Statistical Techniques: A Review of Applications. Expert System with Application. 36: 2–17.
Vapnik V. 1995. The Nature of Statistical Learning Theory. Springer. New York.
Chiu, N. H. and Guao, Y. Y. 2008. State Classification of CBN Grinding with Support Vector Machine. Journal of Material Processing Technology. 201: 601–605.
Radhika Y. and Shashi M. 2009. Atmospheric Temperature Prediction using Support Vector Machines. International Journal of Computing Theory Eng. 1: 1793–8201.
Notton, G., Paoli, C., Ivanova, L., Vasileva, S. and Nivet, M. L. 2013. Neural Network Approach to Estimate 10-min Solar Global Irradiation Values on Tilted Planes. Renewable Energy. 50: 576–584.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
