Now a day the usage of credit cards and net banking for online payments has dramatically increased. The most popular mode of online as well as regular purchase payments is through credit card and security of such transactions is also a major issue as frauds are increasing rapidly. In the existing scenario, fraud is detected after the transaction is done and it makes more difficult to find out fraudulent loses barred by issuing authority. In this paper, we observe the behaviour of credit card transactions using a Hidden Markov Model (HMM) and show how it detects frauds. An HMM is initially trained with the normal behaviour of transaction. If the present credit card transaction is not accepted by the trained HMM with enough high probability, then it declares as a fraudulent transaction. At the same time, we try to ensure that no genuine transactions are rejected.
References
V.Bhusari, S.Patil, 2016, “Study of Hidden Markov Model in Credit Card Fraudulent Detection”.
Trends in online shopping, a Global Nelson Consumer Report, (2008).
European payment cards fraud report, Pay-ments, Cards and Mobiles LLP & Author, (2010)
International Journal of Emerging Technolo-gy and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 7, July 2012)511 Credit Card Fraud Detection Using Hidden Markov Model; Vaibhav Gade, Sonal Chaudhari; All Saint College of Technology, Bhopal (M.P.), In-dia.
L.R. Rabiner, “A Tutorial on Hidden Mar-kov Models and Selected Applications in Speech Recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.
CREDIT CARD FRAUD DETECTION USING HIDDEN MARKOV MODEL by Divya.Iyer, Arti Mohanpurkar, Sneha Ja-nardhan, Dhanashree Rathod, Amruta Sar-deshmukh; Department of Computer engi-neering and Information Technology, MMIT, Pune, India.
Credit Card Fraud Detection Using Hidden Markov Model by Abhinav Srivastava, Am-lan Kundu, Shamik Sural, Senior Member, IEEE, and Arun K. Majumdar, Senior Member, IEEE
Ghosh, S., and Reilly, D.L., 1994. Credit Card Fraud Detection with a Neural-Network, 27th Hawaii International l Con-ference on Information Systems, vol. 3 (2003), pp. 621- 630.
Syeda, M., Zhang, Y. Q., and Pan, Y., 2002 Parallel Granular Networks for Fast Credit Card Fraud Detection, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 572-577 (2002).
Stolfo, S. J., Fan, D. W., Lee, W., Pro-dromidis, A., and Chan, P. K., 2000. Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project, Proceedings of DARPA Information Survivability Conference and Exposition, vol. 2 (2000), pp. 130-144.
Aleskerov, E., Freisleben, B., and Rao, B., 1997. CARD WATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proceedings of IEEE/IAFE: Computational Intelligence for Financial Eng. (1997), pp. 220-226.
M.J. Kim and T.S. Kim, “A Neural Classifi-er with Fraud Density Map for Effective Credit Card Fraud Detection,” Proc.Int’l Conf. Intelligent Data Eng. and Automated Learning, pp. 378-383, 2002.
W. Fan, A.L. Prodromidis, and S.J. Stolfo, “Distributed Data Mining in Credit Card Fraud Detection,” IEEE Intelligent Systems, vol. 14, no. 6, pp. 67-74, 1999.
R. Brause, T. Langsdorf, and M. Hepp, “Neural Data Mining for Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. Tools with Artificial Intelligence, pp. 103-106, 1999.
C. Chiu and C. Tsai, “A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. e-Technology, e-Commerce and e-Service, pp. 177-181, 2004.
C. Phua, V. Lee, K. Smith, and R. Gayler, “A Comprehensive Survey of Data Mining-Based Fraud Detection Research,” https:// www.bsys.monash.edu.au/people/cphua/, Mar. 2007.
S. Stolfo and A.L. Prodromidis, “Agent-Based Distributed Learning Applied to Fraud Detection,” Technical Report CUCS-014-99, Columbia Univ. , 1999.
C. Phua, D. Alahakoon, and V. Lee, “Mi-nority Report in Fraud Detection: Classifica-tion of Skewed Data,” ACM SIGKDD Ex-plorations Newsletter, vol. 6, no. 1, pp. 50-59, 2004.
V. Vatsa, S. Sural, and A.K. Majumdar, “A Game-theoretic Approach to Credit Card Fraud Detection,” Proc. First Int’l Conf. In-formation Systems Security, pp. 263-276, 2005
S.S. Joshi and V.V. Phoha, “Investigating Hidden Markov Models Capabilities in Anomaly Detection,” Proc. 43rd ACM Ann. Southeast Regional Conf., vol. 1, pp. 98-103, 2005.
S.B. Cho and H.J. Park, “Efficient Anomaly Detection by Modeling Privilege Flows Us-ing Hidden Markov Model,” Computer and Security, vol. 22, no. 1, pp. 45-55, 2003.
D. Ourston, S. Matzner, W. Stump, and B. Hopkins, “Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks,” Proc. 36th Ann. Hawaii Int’l Conf. System Sciences, vol. 9, pp. 334-344, 2003.
X.D. Hoang, J. Hu, and P. Bertok, “A Mul-ti-Layer Model for Anomaly Intrusion Detection Using Program Sequences of System Calls,” Proc. 11th IEEE Int’l Conf. Networks, pp. 531- 536, 2003.
T. Lane, “Hidden Markov Models for Hu-man/Computer Interface Modeling,” Proc. Int’l Joint Conf. Artificial Intelligence, Workshop Learning about Users, pp. 35-44, 1999
