Bahnsen, A. C., Aouada, D., Stojanovic, A., &Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142.
Behera, T. K., &Panigrahi, S. (2015, May). Credit card fraud detection: a hybrid approach using fuzzy clustering & neural network. In 2015 Second International Conference on Advances in Computing and Communication Engineering (pp. 494-499). IEEE.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Brzeziński, D. (2010). Mining data streams with concept drift. Cs Put Pozna, 89.
Chen, R. C., Luo, S. T., Liang, X., & Lee, V. C. (2005, October). Personalized approach based on SVM and ANN for detecting credit card fraud. In 2005 International Conference on Neural Networks and Brain (Vol. 2, pp. 810-815). IEEE.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., &Bontempi, G. (2017). Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE transactions on neural networks and learning systems, 29(8), 3784-3797.
Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., &Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications, 41(10), 4915-4928.
Flitman, A. M. (1997). Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis. Computers & Operations Research, 24(4), 367-377.
Ganji, V. R., &Mannem, S. N. P. (2012). Credit card fraud detection using anti-k nearest neighbor algorithm. International Journal on Computer Science and Engineering, 4(6), 1035-1039.
Gurjar, R. N., Sharma, N., &Wadhwa, M. (2014, February). Finding outliers using mutual nearness based ranks detection algorithm. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) (pp. 141-144). IEEE.
Jiang, C., Song, J., Liu, G., Zheng, L., & Luan, W. (2018). Credit card fraud detection: A novel approach using aggregation strategy and feedback mechanism. IEEE Internet of Things Journal, 5(5), 3637-3647.
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. (2018). A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE access, 6, 12103-12117.
Malekian, D., &Hashemi, M. R. (2013, August). An adaptive profile based fraud detection framework for handling concept drift. In 2013 10th International ISC Conference on Information Security and Cryptology (ISCISC) (pp. 1-6). IEEE.
Masud, M., Gao, J., Khan, L., Han, J., &Thuraisingham, B. M. (2010). Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Transactions on Knowledge and Data Engineering, 23(6), 859-874.
NilsonReport, 2019, The Nilson Report: https:// www.nilsonreport.com/ upload/ content promo/ The Nilson Report 01-17-2019.pdf .
Panigrahi, S., Kundu, A., Sural, S., &Majumdar, A. K. (2009). Credit card fraud detection: A fusion 363.
Quah, J. T., &Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert systems with applications, 35(4), 1721-1732.
Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE access, 6, 14277-14284.
Shen, A., Tong, R., & Deng, Y. (2007, June). Application of classification models on credit card fraud detection. In 2007 International conference on service systems and service management (pp. 1-4). IEEE.
Srivastava, A., Kundu, A., Sural, S., &Majumdar, A. (2008). Credit card fraud detection using hidden Markov model. IEEE Transactions on dependable and secure computing, 5(1), 37-48.
Valecha, H., Varma, A., Khare, I., Sachdeva, A., &Goyal, M. (2018, November). Prediction of consumer behaviour using random forest algorithm. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., &Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48.
Wong, M. A., &Hartigan, J. A. (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100-108.
Wei, Q., Yang, Z., Junping, Z., & Yong, W. (2009, August). Mining multi-label concept-drifting streams using ensemble classifiers. In 2009 Sixth international conference on fuzzy systems and knowledge discovery (Vol. 5, pp. 275-279). IEEE.
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data mining and knowledge discovery, 18(1), 30-55.
Ye, N., Zhang, Y., &Borror, C. M. (2004). Robustness of the Markov-chain model for cyber-attack detection. IEEE Transactions on Reliability, 53(1), 116-123.