Abstract - In e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research investigated anomaly detection system in online transactions by proposing the hybrid of Counter Propagation Neural Network with genetic algorithm (CPNN-GA) to overcome challenges being faced by the existing techniques.

Keywords: Anomaly Detection, Counter propagation neural network, Credit card fraud, Genetic algorithm, Online transactions.

[1] Lee M., Ham S. and Jiang Q. (2014). E-commerce Transaction Anomaly Classification. Statistics Department Stanford University, pp 1-5.

[2] Varun C., Arindam B. and Vipin K. (2009). Anomaly Detection: A Survey. ACM Computing Surveys, 41 (3):1-58.

[3] Neda N., Leila B. and Ebrahim N. (2012). Surveying Different Aspects of Anomaly Detection and Its Applications. The Journal of Mathematics and Computer Science, 4 (2):129-138

[4] Malini N. and Pushpa M. (2017). Analysis on Credit Card Fraud Detection TechiniquesBy Data Mining and Big Data Approach. International Journal of Research in Computer Applications And Robotics, 5 (5):38-45.

[5] Vandana S., Sanjeev J., Vilas S., and Dev A. (2015). Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems. Computational Intelligence and Neuroscience, pp 1-12

[6] Malhotra R., Singh N. and Yaduvir S. (2011). Genetic Algorithms: Concepts, Design for Optimization of Process Controllers. Computer and Information Science, 4 (2):39-54.

[7] Razak T. A. and Ahmed G. N. (2014). A Comparative Analysis on Credit Card Fraud Techniques Using Data Mining. International Journal of Data Mining Techniques and Applications, Integrated Intelligent Research (IIR), 3 (2): 398-400. 

[8] Khan M. Z.,  Pathan J. D., Ahmed A. H. E. (2014). Credit Card Fraud Detection System Using Hidden Markov Model and K-Clustering. International Journal of Advanced Research in Computer and Communication Engineering, 3 (2):5458-5461.

[9] Priya B., Malvika D., Shweta P., Nivedita S. and Dhake B. G. (2014). Survey on Credit Card Fraud Detection Using Hidden Markov Model. International Journal of Advanced Research in Computer and Communication Engineering, 3 (5):6445-6448.

[10] Zareapoor M., Seeja. K. R. and Alam M. A. (2012). Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria. International Journal of Computer Applications, 52 (3):35-42.    

[11] Bentley P. J., Kim J. J., Gil-Ho and Choi, J., (2000). Fuzzy Darwinian Detection of Credit Card Fraud. Proceedings of 14th Annual Fall Symposium of the Korean Information Processing Society, 1-4

[12] Srivastava A., Kundu A., Sural S. and Majumadar A. K. (2008). Credit Card Fraud Detection Using Hidden Markov Model. IEEE Transactions on Dependable and Secure Computing, 5 (1):37-48.

[13] Carsten, A.W. (2008). Credit Card Fraud Detection Using Artificial Neural Networks Tuned by Genetic Algorithms.Unpublished PhD Thesis, Hong Kong University of Science and technology, 1-226.

[14] Zhang Y, Fucheng Y. and Liu H.,(2009). Behavior–Based Credit Card Fraud Detection Model. Fifth International Joint Conference on INC, IMS and IDC, pp. 855-858.

[15] Wen-Fang Y. and Wang N. (2009). Research on Credit Card Fraud Detection Model Based on Distance Sum. Proceedings of the International Joint Conference on Artificial Intelligence, pp 353-356.

[16] Brabazon A., Cahill J., Keenan1 P., and Walsh D. (2010). Identifying Online Credit Card Fraud using Artificial Immune Systems. IEEE Congress on Evolutionary Computation (CEC), 1-7.

[`17] Falaki S. O., Alese B. K., Adewale O. S., Ayeni J. O., Aderounmu G. A. and Ismaila W. O. (2012). Probabilistic Credit Card Fraud Detection System in Online Transactions.International Journal of Software Engineering and Its Applications 6, pp 4.

[18] Patel S. and Gond S. (2014). Supervised Machine (SVM) Learning for Credit Card Fraud Detection. International Journal of Engineering Trends and Technology (IJETT), 8 (3):137-139.