A Study of Fraud Types, Challenges and Detection Approaches in Telecommunication
الموضوعات :Kasra Babaei 1 , ZhiYuan Chen 2 , Tomas Maul 3
1 - University of Nottingham Malaysia
2 - University of Nottingham Malaysia
3 - University of Nottingham Malaysia
الکلمات المفتاحية: Fraud Detection, , Machine Learning, , Telecommunication, ,
ملخص المقالة :
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.
المصادر:
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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.
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[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.
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[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.
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[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.
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[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.
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[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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