Abstract
Advanced cyber-threats, specifically targeted to financial institutions, are growing in frequency and sophistication, both globally and in individual countries. To counter this trend, effective solutions are needed that are able to reliably and timely detect frauds across multiple channels that process millions of transactions per day. These security solutions are required to process logs produced by different systems and correlate massive amounts of information in real-time. In this paper, we propose an approach based on the Dempster–Shafer (DS) theory, that results in high performance of the detection process, i.e. high detection rates and low false positive rates. The approach is based on combining multiple (and heterogeneous) data feeds to get to a degree of belief that takes into account all the available evidence. The proposed approach has been validated with respect to a challenging demonstration case, specifically the detection of frauds performed against a mobile money transfer (MMT) service. An extensive experimental campaign has been conducted, using synthetic data generated by a simulator which closely mimics the behavior of a real system, from a major MMT service operator.
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References
Abidi MA, Gonzalez RC (1992) Data fusion in robotics and machine intelligence. Academic Press Professional, Inc
Ahmed M, Huang X, Sharma D, Shutao L (2012) Wireless sensor network internal attacker identification with multiple evidence by dempster-shafer theory. In: Algorithms and architectures for parallel processing. Springer, Berlin, pp 255–263
Altnay H, Demirekler M (2003) Speaker identification by combining multiple classifiers using dempstershafer theory of evidence. Speech Commun 41(4):531–547
CCK (2012) Quarterly sector statistics report. Communications Commission of Kenya
Dempster AP (1967) A generalization of Bayesian inference. Tech. rep, DTIC Document
Dong F, Shatz SM, Xu H (2009) Inference of online auction shills using Dempster–shafer theory. In: Information Technology: new generations 2009 (ITNG’09). Sixth international conference on. IEEE, pp 908–914
Durrant-Whyte H (2001) Multi Sensor Data Fusion. Australian Centre for Field Robotics
Ficco M (2013) Security event correlation approach for cloud computing. Int J High Perform Comput Netw 7(3):173–185
Gaber C, Hemery B, Achemlal M, Pasquet M, Urien P (2013) Synthetic logs generator for fraud detection in mobile transfer services. In: Collaboration technologies and systems (CTS), 2013 international conference on, IEEE, pp 174–179
Gros X (1996) NDT data fusion. Elsevier, Amsterdam
Hu W, Li J, Gao Q (2006) Intrusion detection engine based on Dempster–Shafer’s theory of evidence. In: Proceedings of international conference on communications, circuits and system, pp 1627–1631
Jannin P, Grova C, Gibaud B (2001) Medical applications of NDT data fusion. Springer, Berlin
Merritt C (2011) Mobile money transfer services: the next phase in the evolution of person-to-person payments. J Payments Strategy Syst 5(2):143–160
Panigrahi S, Kundu A, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using Dempster–Shafer theory and Bayesian learning. Inf Fusion 10(4):354–363
Panigrahi S, Kundu A, Sural S, Majumdar AK (2007) Use of Dempster–Shafer theory and Bayesian inferencing for fraud detection in mobile communication networks. In: Information security and privacy. Springer, Berlin, pp 446–460
Penicaud C (2012) State of the industry: results from the 2012 global mobile money adoption survey. In: GSMA mobile money for the unbanked. http://www.gsma.com/mobilefordevelopment/wp-content/uploads/2013/02/MMU_State_of_industry.pdf
Siaterlis C, Maglaris B (2004) Towards multisensor data fusion for dos detection. In: Proceedings of the 2004 ACM symposium on applied computing, pp 439–446
Smith D, Singh S (2006) Approaches to multisensor data fusion in target tracking: a survey. Knowl Data Eng IEEE Trans 18(12):1696–1710
Srivastava RP, Mock TJ, Gao L (2011) The Dempster-Shafer theory: an introduction and fraud risk assessment illustration. Aust Account Rev 21:282–291. doi:10.1111/j.1835-2561.2011.00135.x
Xu L, Krzyzak A, Suen C (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. Syst Man Cybern IEEE Trans 22(3):418–435. doi:10.1109/21.155943
Zhang J (2010) Multi-source remote sensing data fusion: status and trends. Int J Image Data Fusion 1(1):5–24
Zhang B, Srihari S (2002) Class-wise multi-classifier combination based on Dempster–Shafer theory. In: Proceedings of the seventh international conference on control, automation, robotics and vision (ICARV 2002), Singapore, 2–5 Dec 2002, pp 698–703
Zomlot L, Sundaramurthy SC, Luo K, Ou X, Rajagopalan SR (2011) Prioritizing intrusion analysis using dempster-shafer theory. In: Proceedings of the 4th ACM workshop on security and artificial intelligence. ACM, pp 59–70
Acknowledgments
The research leading to these results has received funding from the European Commission within the context of the Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 313034 (Situation AWare Security Operation Center, SAWSOC Project). It has been also partially supported by the TENACE PRIN Project (No. 20103P34XC) funded by the Italian Ministry of Education, University and Research, and by the Embedded Systems in critical domains POR Project (CUP B25B09000100007) funded by the Campania region in the context of the POR Campania FSE 2007–2013, Asse IV and Asse V.
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Coppolino, L., D’Antonio, S., Formicola, V. et al. Use of the Dempster–Shafer theory to detect account takeovers in mobile money transfer services. J Ambient Intell Human Comput 6, 753–762 (2015). https://doi.org/10.1007/s12652-015-0276-9
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DOI: https://doi.org/10.1007/s12652-015-0276-9