Random Articles
Reseach Article
A Novel Hidden Markov Model for Credit Card Fraud Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 59 - Number 3 |
| Year of Publication: 2012 |
| Authors: A. Prakash, C. Chandrasekar |
10.5120/9532-3960
|
A. Prakash, C. Chandrasekar . A Novel Hidden Markov Model for Credit Card Fraud Detection. International Journal of Computer Applications. 59, 3 ( December 2012), 35-41. DOI=10.5120/9532-3960
Abstract
Nowadays the customers prefer the most accepted payment mode via credit card for the convenient way of online shopping, paying bills in easiest way. At the same time the fraud transaction risks using credit card is a main problem which should be avoided. There are many data mining techniques available to avoid these risks effectively. In existing research they modelled the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and shown how it can be used for the detection of frauds. To provide better accuracy and to avoid computational complexity in fraud detection in proposed work semi Hidden Markov model (SHMM) algorithm of anomaly detection is presented which computes the distance between the processes monitored by credit card detection system and the perfect normal processes. With this we are implementing another method for fraud detection is that having a key idea is to factorize marginal log-likelihood using a variation distribution over latent variables. An asymptotic approximation, a factorized information criterion (FIC) obtained by applying the Laplace method to each of the factorized components. Our experimental results demonstrate that we can significantly reduce loss due to fraud through distributed data mining of fraud models.
References
- 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 Using 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 Multi-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 Human/Computer Interface Modeling," Proc. Int'l Joint Conf. Artificial Intelligence, Workshop Learning about Users, pp. 35-44, 1999.
- Fan, W. , Prodromidis, A. L. , and Stolfo, S. J. , 1999. Distributed Data Mining in Credit Card Fraud Detection, IEEE Intelligent Systems, vol. 14, no. 6 (1999), pp. 67-74.
- Brause, R. , Langsdorf, T. , and Hepp, M. , 1999. Neural Data Mining for Credit Card Fraud Detection, Proceedings of IEEE International Conference Tools with Artificial Intelligence (1999), pp. 103-106.
- Chiu, C. , and Tsai, C. , 2004. A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection, Proceedings of IEEE International Conference e-Technology, e-Commerce and e-Service (2004), pp. 177-181.
- Phua, C. , Lee, V. , Smith, K. , and Gayler, R. , 2007. A Comprehensive Survey of Data Mining-Based Fraud Detection Research (2007), March.
- Rabiner, L. R. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of IEEE, vol. 77, no. 2 (1989), pp. 257-286.
- Ourston, D. , Matzner, S. , Stump, W. , and Hopkins, B. , 2003. Applications of Hidden Markov Models to Detecting Multi- Stage Network Attacks, Proceedings of 36th Annual Hawaii International Conference System Sciences, vol. 9 (2003), pp. 334-344.
- Cho, S. B. , and Park, H. J. , 2003. Efficient Anomaly Detection by Modeling Privilege Flows Using Hidden Markov Model, Computer and Security, vol. 22, no. 1 (2003), pp. 45-55.
- Kim, M. J. , and Kim, T. S. , 2002. A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection, Proceedings of International Conference on Intelligent Data Eng. and Automated Learning, (2002), pp. 378-383.
- Fujimaki, Ryohei and Morinaga, Satoshi. Factorized asymptotic bayesian inference for mixture models. In AISTATS, 2012.
- Rabiner, L. R. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE, 77:257{86, 1989.
- Guedon, Y. (2003). "Estimating Hidden Semi-Markov Chains From Discrete Sequences " Journal of Computational & Graphical Statistics 12(3): 604-639.
- M. Beal. Variational Algorithms for Approximate Bayesian Inference. PhD thesis, Gatsby Computational Neuroscience Unit, University College London, 2003.
- A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from imcomplete data via the EM algorithm. Journal of the Royal Statistical Society, B39(1):1–38, 1977.
- H. Jeffreys. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 186:453–461, 1946.
Index Terms
Keywords