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2022 | OriginalPaper | Buchkapitel

AntiPhiMBS-TRN: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Transaction Level

verfasst von : Tej Narayan Thakur, Noriaki Yoshiura

Erschienen in: Intelligent Information and Database Systems

Verlag: Springer Nature Switzerland

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Abstract

With the continuous improvement and growth at a rapid pace in the utility of mobile banking payment technologies, fraudulent mobile banking transactions are being multiplied using bleeding-edge technologies sharply and a significant economic loss is made every year around the world. Phishers seek new vulnerabilities with every advance in fraud prevention and have become an even more pressing issue of security challenges for banks and financial institutions. However, researchers have focused mainly on the prevention of fraudulent transactions on the online banking system. This paper proposes a new anti-phishing model for mobile banking systems at the transaction level (AntiPhiMBS-TRN) that mitigates fraudulent transactions in the mobile banking payment system. This model applies a unique id for the transactions and an application id for the bank application known to the bank, bank application, users, and the mobile banking system. In addition, AntiPhiMBS-TRN also utilizes the international mobile equipment identity (IMEI) number of the registered mobile device to prevent fraudulent transactions. Phishers cannot execute fraudulent transactions without knowing the unique id for the transaction, application id, and IMEI number of the mobile device. This paper employs a process meta language (PROMELA) to specify system descriptions and security properties and builds a verification model of AntiPhiMBS-TRN. Finally, AntiPhiMBS-TRN is successfully verified using a simple PROMELA interpreter (SPIN). The SPIN verification results prove that the proposed AntiPhiMBS-TRN is error-free, and banks can implement the verified model for mitigating fraudulent transactions in the mobile banking system globally.

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Vishwakarma, P.P., Tripathy, A.K., Vemuru, S.: A Layered approach to fraud analytics for nfc-enabled mobile payment system. In: Negi, A., Bhatnagar, R., Parida, L. (eds.) ICDCIT 2018. LNCS, vol. 10722, pp. 127–131. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72344-0_9CrossRef

Delecourt, S., Guo, L.: Building a robust mobile payment fraud detection system with adversarial examples. In: 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp. 103–106. IEEE. Sardinia (2019). https://doi.org/10.1109/AIKE.2019.00026

Zhou, H., Chai, H.F., Qiu, M.I.: Fraud detection within bankcard enrollment on mobile device based payment using machine learning. Front. Inf. Technol. Electron. Eng. 19(12), 1537–1545 (2018). https://doi.org/10.1631/FITEE.1800580CrossRef

Eneji, S.E., Angib, M.U., Ibe, W.E., Ekwegh, K.C.: A study of electronic banking fraud, fraud detection and control. Int. J. Innov. Sci. Res. Technol. 4(3), 708–711 (2019)

Ali, M., Hussin, N., Abed, I.: E-banking fraud detection: a short review. Int. J. Innov. Creat. Change 6(8), 67–87 (2019)

Pracidelli, L.P., Lopes, F.S.: Electronic payment fraud detection using supervised and unsupervised learning. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S., Orovic, I., Moreira, F. (eds.) WorldCIST 2020. AISC, vol. 1160, pp. 88–101. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45691-7_9CrossRef

Guo, C., Wang, H., Dai, H., Cheng, S., Wang, T.: Fraud risk monitoring system for e-banking transactions. In: 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, pp. 100–105. IEEE, Athens (2018). https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00030

Seo, J.H., Choi, D.: Feature selection for chargeback fraud detection based on machine learning algorithms. Int. J. Appl. Eng. Res. 11(22), 10960–10966 (2016)

Minastireanu, E., Mesnita, G.: An analysis of the most used machine learning algorithms for online fraud detection. Informatica Economica 23(1), 5–16 (2019). https://doi.org/10.12948/issn14531305/23.1.2019.01CrossRef

10.

Zhou, X., Zhang, Z., Wang, L., Wang, P.: A model based on Siamese neural network for online transaction fraud detection. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–7, IEEE, Budapest (2019). https://doi.org/10.1109/IJCNN.2019.8852295

11.

Khattri, V., Singh, D.K.: A novel distance authentication mechanism to prevent the online transaction fraud. In: Siddiqui, N.A., Tauseef, S.M., Abbasi, S.A., Rangwala, A.S. (eds.) Advances in Fire and Process Safety. STCEE, pp. 157–169. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7281-9_13CrossRef

12.

Kanika, Singla, J.: A survey of deep learning based online transactions fraud detection systems. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp. 130–136. IEEE, London (2020). https://doi.org/10.1109/ICIEM48762.2020.9160200

13.

Hartl, V.M.I.A., Schmuntzsch, U.: Fraud protection for online banking. In: Tryfonas, T. (ed.) HAS 2016. LNCS, vol. 9750, pp. 37–47. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39381-0_4CrossRef

14.

Kataria, S., Nafis, M.T.: Internet banking fraud detection using deep learning based on decision tree and multilayer perceptron. In: 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1298–1302. IEEE, New Delhi (2019)

15.

Masoud, K., Mehdi, F.: Fraud detection in banking using kNN (k-nearest neighbor) algorithm. In: International Conference on Research in Science and Technology, vol. 5, pp. 26–34. Scientific Information Database, London (2016)

16.

Eshghi, A., Kargari, M.: Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty. Expert Syst. Appl. 121, 382–392 (2019). https://doi.org/10.1016/J.ESWA.2018.11.039CrossRef

17.

Kargari, M., Eshghi, A.: A model based on clustering and association rules for detection of fraud in banking transactions. In: Proceedings of the 4th World Congress on Electrical Engineering and Computer Systems and Sciences EECSS, vol. MVML 104, Madrid, Spain (2018). https://doi.org/10.11159/MVML18.104

18.

Sarma, D., Alam, W., Saha, I., Alam, M.N., Alam, M.J., Hossain, S.: Bank fraud detection using community detection algorithm. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 642–646. IEEE, Coimbatore (2020). https://doi.org/10.1109/ICIRCA48905.2020.9182954

19.

Gyamfi, N.K., Abdulai, J.: Bank fraud detection using support vector machine. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 37–41. IEEE, Vancouver (2018). https://doi.org/10.1109/IEMCON.2018.8614994

20.

Shaji, J., Panchal, D.: Improved fraud detection in e-commerce transactions. In: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 121–126. IEEE, Mumbai (2017). https://doi.org/10.1109/CSCITA.2017.8066537

21.

Susto, G.A., Terzi, M., Masiero, C., Pampuri, S., Schirru, A.: A fraud detection decision support system via human on-line behavior characterization and machine learning. In: 2018 First International Conference on Artificial Intelligence for Industries (AI4I), pp. 9–14. IEEE, Laguna Hills (2018). https://doi.org/10.1109/AI4I.2018.8665694

22.

Sapozhnikova, M.U., Nikonov, A.V., Vulfin, A.M., Gayanova, M.M., Mironov, K.V., Kurennov, D.V.: Anti-fraud system on the basis of data mining technologies. In: 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 243–248. IEEE, Bilbao (2017). https://doi.org/10.1109/ISSPIT.2017.8388649

23.

Mubalaike, A.M., Adali, E.: Deep learning approach for intelligent financial fraud detection system. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. 598–603. IEEE, Sarajevo (2018). https://doi.org/10.1109/UBMK.2018.8566574

24.

2019 Iovation financial services fraud and consumer trust report. https://content.iovation.com/resources/2019-iovation-Financial-Services-Fraud-and-Consumer-Trust-Report.pdf. Accessed 14 Dec 2021

25.

Fraud-The Facts 2021: the definitive overview of payment industry fraud report. https://www.ukfinance.org.uk/policy-and-guidance/reports-publications/fraud-facts-2021. Accessed 14 Apr 2022

Titel: AntiPhiMBS-TRN: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Transaction Level
verfasst von: Tej Narayan Thakur
Noriaki Yoshiura
Verlag: Springer Nature Switzerland
Buch: Intelligent Information and Database Systems
Print ISBN: 978-3-031-21966-5

Electronic ISBN: 978-3-031-21967-2

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-21967-2_48

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