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A Review of Telecommunication Fraud Detection Techniques

Shrirama Manjunatha Bhat

Abstract


Telecommunication fraud pertains to its misuse of telecommunication items such as cell phones, mobile, and other services with the goal of defrauding a communication service provider or its consumer. The field of technology is growing day by day. So, there occurred an immense change in the telecom industry with the invention and expansion of mobile communication service. This caused increase in the telecommunication fraud which leads to the loss of billions of dollars worldwide. In this review paper we are focusing on some methods or the approaches (back propagation algorithm, machine learning technology, usage profiling based on call detail record and graph neural network) used to detect and prevent such frauds.


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References


Mohamed, A., Bandi, A. F. M., Tamrin, A. R., Jaafar, M. D., Hasan, S., & Jusof, F. (2009, December). Telecommunication fraud prediction using backpropagation neural network. In 2009 International Conference of Soft Computing and Pattern Recognition (pp. 259-265). IEEE.

Bikov, T. D., Iliev, T. B., Mihaylov, G. Y., & Stoyanov, I. S. (2019, May). Phishing in Depth–Modern Methods of Detection and Risk Mitigation. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 447-450). IEEE.

Qayyum, S., Mansoor, S., Khalid, A., Halim, Z., & Baig, A. R. (2010, June). Fraudulent call detection for mobile networks. In 2010 International Conference on Information and Emerging Technologies (pp. 1-5). IEEE.

Tarmazakov, E. I., & Silnov, D. S. (2018). Modern approaches to prevent fraud in mobile communications networks. In 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 379-381). IEEE.

Ab Raub, R., Hamzah, A. H. N., Jaafar, M. D., & Baharim, K. N. (2016, November). Using subscriber usage profile risk score to improve accuracy of telecommunication fraud detection. In 2016 International Conference on Computational Intelligence and Cybernetics (pp. 127-131). IEEE.

Ji, S., Li, J., Yuan, Q., & Lu, J. (2020, July). Multi-Range Gated Graph Neural Network for Telecommunication Fraud Detection. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.

Menon, K. D., Raj Jain, A., & Kumar Pareek, D. (2019). Quantitative analysis of student data mining.

Pai H, A., HS, S., Soman, S., Pareek, D., & Kumar, P. (2019). Analysis of causes and effects of longer lead time in software process using FMEA. Piyush Kumar, Analysis of Causes and Effects of Longer Lead Time in Software Process Using FMEA (May 17, 2019).

Pai H, A., HS, S., Soman, S., Pareek, D., & Kumar, P. (2019). ROC Structure Analysis of Lean Software Development in SME’s Using Mathematical CHAID Model. Piyush Kumar, ROC Structure Analysis of Lean Software Development in SME’s Using Mathematical CHAID Model (May 17, 2019).

HS, S., Soman, S., & Kumar Pareek, D. (2019). Fast and efficient parallel alignment model for aligning both long and short sentences.


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