Abstract
Cybercrimes and fraud techniques are major threats to telecommunications sectors in the last decade, one of those fraud approaches called Wangiri fraud. Wangiri is a common type of fraud techniques in telecommunications sector, the definition is originated from a Japanese word that means (one & cut); as the fraudsters depend on a single ring method to gain illegal money from the subscribers. Consequently, the approaches that are used to detect fraud cases are used to classify subscribers based on their behaviors such as data extraction that identifies patterns in large datasets through a combination of statistical methods, artificial intelligence and databases. Neural networks are used to process & evaluate the given datasets; in order to uncover ambiguous communication and secret data patterns. This paper proposes the usage of AI neural networks to overcome the highly predictive wangiri fraud in a telecom dataset and to make an effective and convenient classification. ISTAT was used to test both accuracy and efficiency of the proposed method.
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References
Jain, V.: Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining. Int. J. Inf. Technol. 9(3), 303–310 (2017) https://doi.org/10.1007/s41870-017-0036-5
Fraud detection gap between auditor and fraud detection models: evidence from gulf cooperation council. Asian J. Account. Governance 13, 1–13 (2020). https://doi.org/10.17576/ajag-2020-13-01
Guo, K., Wang, W.: Research on telecom fraud detection model based on cellular network data. J. Networking Telecommun. 2(1), p. 12 (2020). https://doi.org/10.18282/jnt.v2i1.835
Gangadharan, N.: What is Wangiri Fraud? How does it impact telecom operators? In: Subex Limited. https://www.subex.com/blog/What-is-wangiri-fraud-how-does-it-impact-for-telecom-operators/. Accessed 9 Mar 2020
Detect & Protect Against Wangiri Callback Fraud. In: Enghouse Networks. https://www.enghousenetworks.com/enghouse-resources/blog/detect-protect-against-wangiri-callback-fraud/. Accessed 15 Apr 2020
Ye, N., Cheng, L., Zhao, Y.: Identity construction of suspects in telecom and internet fraud discourse: from a sociosemiotic perspective. Soc. Semiot. 29, 319–335 (2019). https://doi.org/10.1080/10350330.2019.1587847
Zoldi, S.: Using anti-fraud technology to improve the customer experience. Comput. Fraud Secur. 2015, 18–20 (2015). https://doi.org/10.1016/s1361-3723(15)30067-1
(2020) Detect & Protect Against Wangiri Callback Fraud. In: Enghouse Networks. https://www.enghousenetworks.com/enghouse-resources/blog/detect-protect-against-wangiri-callback-fraud/. Accessed 8 Sep 2020
Subudhi, S., Panigrahi, S.: Quarter-sphere support vector machine for fraud detection in mobile telecommunication networks. Procedia Comput. Sci. 48, 353–359 (2015). https://doi.org/10.1016/j.procs.2015.04.193
Mawgoud, A.A., Ali, I.: Statistical insights and fraud techniques for telecommunications sector in Egypt. In: 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), Aswan, Egypt, pp. 143–150 (2020) https://doi.org/10.1109/ITCE48509.2020.9047767
Ghosh, M.: Mobile ID fraud: the downside of mobile growth. Comput. Fraud Secur. 2010(12), 8–13 (2010). https://doi.org/10.1016/s1361-3723(10)70155-x
Kurilovas, E.: Advanced machine learning approaches to personalise learning: learning analytics and decision making. Behaviour & Information Technology 38(4), 410–421 (2018) https://doi.org/10.1080/0144929x.2018.1539517
Kabari, L., Nuka Nanwin, D., Uduak Nquoh, E.: Telecmmunications subscription fraud detection using artificial nueral networks. Trans. Mach. Learn. Artif. Intell. 3(6), (2015). https://doi.org/10.14738/tmlai.36.1695.
WIEDMAN, C.I.: Instructional case: detecting earnings manipulation. Issues Acc. Educ. 14(1), 145–176 (1999)
Han, Y., Ting, S.H., Pandharipande, A.: Cooperative spectrum sharing protocol with secondary user selection. IEEE Trans. Wireless Commun. 9(9), 2914–2923 (2010). https://doi.org/10.1109/TWC.2010.080210.091741
Sahin, Y., Bulkan, S., Duman, E.: A cost- sensitive decision tree approach for fraud detection. Expert Syst. Appl. 40(15), 5916–5923 (2013)
Coppolino, L., et al.: Use of the dempster-shafer theory for fraud detection: the mobile money transfers case study. In: Intelligent Distributed Computing VIII. Springer, Cham, pp. 465–474 (2015)
Ghosh, M., et al.: Empirical analysis of ensemble methods for the classification of robocalls in telecommunications. Int. J. Electr. Comput. Eng. (2088–8708), 9 (2019)
Tarmazakov, E.I., Silnov, D.S.: Modern approaches to prevent fraud in mobile communications networks. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, pp. 379–381 (2018) https://doi.org/10.1109/EIConRus.2018.8317111
Liu, M., Liao, J., Wang, J., Qi, Q.: AGRM: attention-based graph representation model for telecom fraud detection. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, pp. 1–6 (2019). https://doi.org/10.1109/ICC.2019.8761665
Subudhi, S., Panigrahi, S.: A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network. Vietnam J. Comput. Sci. 5(3–4), 205–217 (2018)
Mawgoud, A.A., et al.: Cyber security risks in MENA region: threats, challenges and countermeasures. In: International Conference on Advanced Intelligent Systems and Informatics. Springer, Cham, pp. 912–921 (2019)
El Karadawy, A.I., Mawgoud, A.A., Rady, H.M.: An empirical analysis on load balancing and service broker techniques using cloud analyst simulator. In: 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). IEEE, pp. 27–32 (2020)
Mawgoud, A.A., Taha, M.H.N., Khalifa, N.E.M.: Security threats of social internet of things in the higher education environment. In: Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications. Springer, Cham, pp. 151–171 (2020)
Glaser, E.M., Van Der Loos, H.: A semi-automatic computer-microscope for the analysis of neuronal morphology. IEEE Trans. Biomed. Eng. BME-12(1), 22–31 (1965). https://doi.org/10.1109/TBME.1965.4502337
Mawgoud, A.A.: A survey on Ad-hoc cloud computing challenges. In: 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). IEEE, pp. 14–19 (2020)
De Nadai, M.: Mobile phone activity in a city. Kaggle.com, 2020. https://www.kaggle.com/marcodena/mobile-phone-activity. Accessed 09 Aug 2020
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Mawgoud, A.A., Abu-Talleb, A., Tawfik, B.S. (2021). A Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_19
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