Abstract
Currently, online banking has become extremely popular all over the world and plays a significant role in people‘s daily lives. However, the user behaviors have yet to be studied carefully in existing works. In this paper, we provide a large-scale, comprehensive measurement study of online banking users based on a two-week long dataset consisting of transactions conducted by personal users in one of the top banks in China. We demonstrate the customer behaviors mostly comply with the heavy-tail distribution which implies abnormal activities. In further analysis of those activities, we figure out that most of them are generated by two types of accounts, i.e., corporate accounts paying salaries and dishonest bank employees plastering the achievement. We extract a set of features to classify the two types of abnormal accounts from the benign ones. The experimental result illustrates that our system can accurately detect them with only \(0.5\%\) false positive rate.
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References
Cabanes, G., Bennani, Y., Grozavu, N.: Unsupervised learning for analyzing the dynamic behavior of online banking fraud. In: Ding, W., et al. (eds.) ICDM Workshops, pp. 513–520. IEEE Computer Society (2013)
Carminati, M., Caron, R., Maggi, F., Epifani, I., Zanero, S.: BankSealer: a decision support system for online banking fraud analysis and investigation. Comput. Secur. 53, 175–186 (2015)
Carminati, M., Valentini, L., Zanero, S.: A supervised auto-tuning approach for a banking fraud detection system. In: Dolev, S., Lodha, S. (eds.) CSCML 2017. LNCS, vol. 10332, pp. 215–233. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60080-2_17
Dhankhad, S., Mohammed, E., Far, B.: Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 122–125. IEEE (2018)
Gao, J., Cao, Y., Tung, W.-W., Hu, J.: Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond. Wiley, Hoboken (2007)
Hanafizadeh, P., Keating, B.W., Khedmatgozar, H.R.: A systematic review of internet banking adoption. Telematics and Inform. 31(3), 492–510 (2014)
Herington, C., Weaven, S.: E-retailing by banks: e-service quality and its importance to customer satisfaction. Eur. J. Mark. 43(9/10), 1220–1231 (2009)
Jyothsna, V., Prasad, V.R., Prasad, K.M.: A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28(7), 26–35 (2011)
Karlsen, K.N., Killingberg, T.: Profile based intrusion detection for internet banking systems (2008)
Kock, R.: 80–20 Principle: The Secret to Success by Achieving More with Less. Crown Business, New York City (1999)
Kovach, S., Ruggiero, W.V.: Online banking fraud detection based on local and global behavior. In: Proceedings of the Fifth International Conference on Digital Society, Guadeloupe, France, pp. 166–171 (2011)
Pikkarainen, K., Pikkarainen, T., Karjaluoto, H., Pahnila, S.: The measurement of end-user computing satisfaction of online banking services: empirical evidence from finland. Int. J. Bank Mark. 24(3), 158–172 (2006)
Rodrigues, L.F., Costa, C.J., Oliveira, A.: How does the web game design influence the behavior of e-banking users? Comput. Hum. Behav. 74, 163–174 (2017)
Carminati, M., Baggio, A., Maggi, F., Spagnolini, U., Zanero, S.: FraudBuster: temporal analysis and detection of advanced financial frauds. In: Giuffrida, C., Bardin, S., Blanc, G. (eds.) DIMVA 2018. LNCS, vol. 10885, pp. 211–233. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93411-2_10
Wei, W., Li, J., Cao, L., Ou, Y., Chen, J.: Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4), 449–475 (2013)
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Wang, Y., Wang, L., Xu, Z., An, W. (2019). Understanding User Behavior in Online Banking System. In: Guo, F., Huang, X., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2018. Lecture Notes in Computer Science(), vol 11449. Springer, Cham. https://doi.org/10.1007/978-3-030-14234-6_35
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DOI: https://doi.org/10.1007/978-3-030-14234-6_35
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