A Study of Fraud Types, Challenges and Detection Approaches in Telecommunication
Subject Areas : Machine learningKasra Babaei 1 , ZhiYuan Chen 2 , Tomas Maul 3
1 - University of Nottingham Malaysia
2 - University of Nottingham Malaysia
3 - University of Nottingham Malaysia
Keywords: Fraud Detection, , Machine Learning, , Telecommunication, ,
Abstract :
Fraudulent activities have been rising globally resulting companies losing billions of dollars that can cause severe financial damages. Various approaches have been proposed by researchers in different applications. Studying these approaches can help us obtain a better understanding of the problem. The aim of this paper is to investigate different aspects of fraud prevention and detection in telecommunication. This study presents a review of different fraud categories in telecommunication, the challenges that hinder the detection process, and some proposed solutions to overcome them. Also, the performance of some of the state-of-the-art approaches is reported followed by our guideline and recommendation in choosing the best metrics.
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[14] H. M. Marah, O. M. Elrajubi, and A. A. Abouda, “Fraud detection in international calls using fuzzy logic,” in Proceedings - International Conference on Computer Vision and Image Analysis Applications, ICCVIA 2015, 2015.
[15] L. Cortesão, F. Martins, A. Rosa, and P. Carvalho, “Fraud management systems in telecommunications: a practical approach,” in 12th International Conference on Telecommunications, 2005, pp. 167–182.
[16] T. Fawcett and F. Provost, “Adaptive Fraud Detection,” Data Min. Knowl. Discov., vol. 1, no. 3, pp. 291–316, 1997.
[17] C. S. Hilas and J. N. Sahalos, “An Application of Decision Trees for Rule Extraction Towards Telecommunications Fraud Detection,” in Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part II, B. Apolloni, R. J. Howlett, and L. Jain, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 1112–1121.
[18] J. Lopes, O. Belo, and C. Vieira, “Applying User Signatures on Fraud Detection in Telecommunications Networks,” in Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 -- September 3, 2011.
Proceedings, P. Perner, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 286–299.
[19] P. Saravanan, V. Subramaniyaswamy, N. Sivaramakrishnan, M. Arun Prakash, and T. Arunkumar, “Data mining approach for subscription-fraud detection in telecommunication sector,” Contemp. Eng. Sci., vol. 7, no. 9–12, pp. 515–522, 2014.
[20] P. A. Estévez, C. M. Held, and C. A. Perez, “Subscription fraud prevention in telecommunications using fuzzy rules and neural networks,” Expert Syst. Appl., vol. 31, no. 2, pp. 337–344, 2006.
[21] F. M. Kau and O. P. Kogeda, “Impact of Subscription Fraud in Mobile Telecommunication Companies,” in 2019 Open Innovations (OI), 2019, pp. 42–47.
[22] S. Patnaik, S. Subudhi, and S. Panigrahi, “Quarter-Sphere Support Vector Machine for Fraud Detection in Mobile Telecommunication Networks,” Procedia Comput. Sci., vol. 48, pp. 353–359, 2015.
[23] M. I. M. Yusoff, I. Mohamed, and M. R. A. Bakar, “Fraud detection in telecommunication industry using Gaussian mixed model,” in 2013 International Conference on Research and Innovation in Information Systems (ICRIIS), 2013, pp. 27–32.
[24] M. Arafat, A. Qusef, and G. Sammour, “Detection of Wangiri Telecommunication Fraud Using Ensemble Learning,” in 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019, pp. 330–335.
[25] G. Phil and H. Mark, “Classification Detection and Prosecution of Fraud on Mobile Networks,” in Proceedings of ACTS Mobile Summit, Sorrento Italy, 1999.
[26] S. Kamas and M. A. Aydin, “SPIT detection and prevention,” Istanbul Univ. - J. Electr. Electron. Eng., vol. 17, pp. 3213–3218, 2017.
[27] L. Carvajal, L. Chen, C. Varol, and D. Rawat, “Detecting unprotected SIP-based Voice over IP traffic,” in 4th International Symposium on Digital Forensics and Security, ISDFS 2016 - Proceeding, 2016, pp. 44–48.
[28] G. Cho, J. Cho, Y. Song, D. Choi, and H. Kim, “Combating online fraud attacks in mobile-based advertising,” Eurasip J. Inf. Secur., vol. 2016, no. 1, pp. 1–9, 2016.
[29] M. Yelland, “Fraud in mobile networks,” Comput. Fraud Secur., vol. 2013, no. 3, pp. 5–9, 2013.
[30] H. Farvaresh and M. M. Sepehri, “A data mining framework for detecting subscription fraud in telecommunication,” Eng. Appl. Artif. Intell., vol. 24, no. 1, pp. 182–194, 2011.
[31] L. P. Mendes, J. Dias, and P. Godinho, “Bi-level clustering in telecommunication fraud,” in ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems, 2012, pp. 126–131.
[32] D. Olszewski, “Fraud detection using self-organizing map visualizing the user profiles,” Knowledge-Based Syst., vol. 70, pp. 324–334, 2014.
[33] D. Olszewski, “A probabilistic approach to fraud detection in telecommunications,” Knowledge-Based Syst., vol. 26, pp. 246–258, 2012.
[34] C. S. Hilas and J. N. Sahalos, “User profiling for fraud detection in telecommunication networks,” in In: 5th Int. Conf. technology and automation, 2005, pp. 382–387.
[35] R. Sallehuddin, S. Ibrahim, A. M. Zain, and A. H. Elmi, “Detecting SIM box fraud by using support vector machine and artificial neural network,” J. Teknol., vol. 74, no. 1, pp. 137–149, 2015.
[36] J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Comput. Surv., vol. 46, no. 4, pp. 44:1--44:37, Mar. 2014.
[37] Z. Shaeiri, J. Kazemitabar, S. Bijani, and M. Talebi, “Behavior-Based Online Anomaly Detection for a Nationwide Short Message Service,” J. AI Data Min., vol. 7, no. 2, pp. 239–247, 2019.
[38] L. Manunza, S. Marseglia, and S. P. Romano, “Kerberos: A real-time fraud detection system for IMS-enabled VoIP networks,” J. Netw. Comput. Appl., vol. 80, pp. 22–34, 2017.
[39] K.-I. Kim, T. Kim, N.-W. Cho, and M. Kim, “Toll Fraud Detection of VoIP Service Networks in Ubiquitous Computing Environments,” Int. J. Distrib. Sens. Networks, vol. 2015, 2015.
[40] J. Li, K.-Y. Huang, J. Jin, and J. Shi, “A survey on statistical methods for health care fraud detection,” Health Care Manag. Sci., vol. 11, no. 3, pp. 275–287, 2008.
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