Bhatla, T. P., Prabhu, V., and Dua, A. 2003. Understanding credit card frauds. Cards Bus. Rev., 1(6).
Holmes, T. E. 2015. Credit card fraud and ID theft statistics.
Consumer Sentinel Network Data Book for January - December 2016. Federal Trade Commission, 2017.
Ilonen, J., Paalanen, P., Kamarainen, J. K., and Kalviainen, H. 2006. Gaussian mixture pdf in one-class classification: computing and utilizing confidence values. in Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. 2, 577–580.
Clifton, D. A., Hugueny, S., and Tarassenko, L. 2011. Novelty detection with multivariate extreme value statistics. J. Signal Process. Syst., 65(3), 371–389.
Tran, K. P. 2016. The efficiency of the 4-out-of-5 Runs Rules scheme for monitoring the Ratio of Population Means of a Bivariate Normal distribution. Int. J. Reliab. Qual. Saf. Eng.
Tran, K. P. 2017. Run Rules median control charts for monitoring process mean in manufacturing. Qual. Reliab. Eng. Int., 33(8), 2437–2450.
Tran, K. P. 2018. Designing of Run Rules t control charts for monitoring changes in the process mean. Chemom. Intell. Lab. Syst., 174, 85–93.
Tran, K. P., Castagliola, P., and Balakrishnan, N., On the performance of Shewhart median chart in the presence of measurement errors. Qual. Reliab. Eng. Int., 33(5), 1019–1029.
Tran, K. P., Castagliola, P., and Celano, G. 2016. Monitoring the Ratio of Two Normal Variables Using EWMA Type Control Charts. Qual. Reliab. Eng. Int., 32(5), 1853–1869.
Tran, K. P., Castagliola, P., and Celano, G. 2016. Monitoring the Ratio of Two Normal Variables Using Run Rules Type Control Charts. Int. J. Prod. Res., 54(6), 1670–1688.
Tran, K. P., Castagliola, P., and Celano, G. 2018. Monitoring the Ratio of Population Means of a Bivariate Normal distribution using CUSUM Type Control Charts. Stat. Pap., 59(1), 387–413.
Tran K. P. and Knoth, S. 2018. Steady-state ARL analysis of ARL-unbiased EWMA-RZ control chart monitoring the ratio of two normal variables. Qual. Reliab. Eng. Int., 34(3), 377–390.
Chandola, V., Banerjee, A., and Kumar, V. 2016. Anomaly Detection. in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G. I. Webb, Eds. Boston, MA: Springer US, 1–15.
Tran P. H., and Tran, K. P. 2016. The Efficiency of CUSUM schemes for monitoring the Coefficient of Variation. Appl. Stoch. Models Bus. Ind., 32(6), 870–881.
Castagliola, P., Tran, K. P., Celano, G., Rakitzis, A. C., and Maravelakis, P. E. 2017. An EWMA-Type Sign Chart with Exact Run Length Properties. in Proceedings of the International Symposium on Statistical Process Monitoring 2017.
Aleskerov, E., Freisleben, B., and Rao, B. 1997. Cardwatch: A neural network based database mining system for credit card fraud detection. in Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997, 220–226.
Bahnsen, A. C., Aouada, D., and Ottersten, B. 2015. Example-dependent cost-sensitive decision trees. Expert Syst. Appl., 42(19), 6609–6619.
S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decis. Support Syst., 50(3), 602–613.
Hejazi, M. and Singh, Y. P. 2013. One-class support vector machines approach to anomaly detection. Appl. Artif. Intell., 27(5), 351–366.
Pozzolo, A. D., Caelen, O., Borgne, Y. A. L., Waterschoot, S., and Bontempi, G. 2014. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl., 41(10), 4915–4928.
Rabaoui, A., Davy, M., Rossignol, S., and Ellouze, N. 2008. Using one-class SVMs and wavelets for audio surveillance. IEEE Trans. Inf. Forensics Secur., 3(4), 763–775.
Trinh, V. V., Tran, K. P., and Truong, T. H. 2017. Data driven hyperparameter optimization of one-class support vector machines for anomaly detection in wireless sensor networks. in Proceedings of the 2017 International Conference on Advanced Technologies for Communications, Quy Nhon, Vietnam.
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C. 2001. Estimating the support of a high-dimensional distribution. Neural Comput., 13(7), 1443–1471.
Tracy, N., Young, J., and Mason, R. 1992. Multivariate Control Charts for Individual Observations. J. Qual. Technol., 24(2), 88–95.
Pozzolo A. D., Boracchi, G., Caelen, O., Alippi, C., and Bontempi, G. 2015. Credit card fraud detection and concept-drift adaptation with delayed supervised information. Neural Netw. IJCNN Interna- Tional Jt. Conf. IEEE, 1–8.
Lowry C. A., Woodall, W., Champ, C. W., and Rigdon, S. E. 1992. A Multivariate Exponentially Weighted Moving Average control chart. Technometrics, 34(1), 46–53.
Tran K., Castagliola, P., Celano, G., and Khoo, M. 2018. Monitoring compositional data using multivariate exponentially weighted moving average scheme. Qual. Reliab. Eng. Int., 34(3), 391–402.
Elhamahmy M., Elmahdy, H. N., and Saroit, I. A. 2010. A new approach for evaluating intrusion detection system. Int. J. Artif. Intell. Syst. Mach. Learn., 2(11).