Abstract

Anomaly detection is one of the major requirements of the current age that witnesses a huge increase in online transactions. Data imbalance also poses a huge challenge in the detection process. This paper presents a hybrid metaheuristic algorithm that performs effective anomaly detection on highly imbalanced data. Particle Swarm Optimization is used as the operating algorithm. This algorithm is hybridized by modifying the probabilistic selection using Simulated Annealing. A comparison study was carried out and it was observed that the simulated annealing based PSO showed much prominence when operated on both dominant and submissive data.

References

• ECB February 2014. Third Report on Fraud, European Central Bank.
• Véronique Van Vlasselaer, Cristián Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens. 2015. APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions,Decision Support Systems, Volume 75 , Pages 38-48.
• Bolton, R. J. Hand, D.J. 2001. Unsupervised profiling methods for fraud detection, Proceedings of the VII Conference on Credit Scoring and Credit Control, pp. 235–255 (Edinburgh, United Kingdom).
• Weston, D. J., Hand, D. J., Adams, N. M. Whitrow, C., Juszczak, P.2008. Plastic card fraud detection using peer group analysis, ADAC 2 (1) 45–62.
• Quah, J. T., Sriganesh, M. 2008. Real-time credit card fraud detection using computational intelligence, Expert Syst. Appl. 35 (4) 1721–1732.
• Zaslavsky, V., Strizhak, A. 2006. Credit card fraud detection using self-organizing maps, Inf. Secur. 18 48.
• Aleskerov, E., Freisleben, B. Rao,.B . 1997. Cardwatch: a neural network based database mining system for credit card fraud detection, Computational Intelligence for Financial Engineering (CIFEr), Proceedings of the , pp. 220–226.
• Brause, R., Langsdorf, T., M. Hepp,M . 1999. Neural data mining for credit card fraud detection, Proceedings. 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 103–106.
• Dorronsoro, J. R., Ginel, F., Sgnchez, C., Cruz, C. 1997 .Neural fraud detection in credit card operations, IEEE Trans. Neural Netw. 8 (4) 827–834.
• Ghosh, S., Reilly,D.L. 1994. Credit card fraud detection with a neural-network, Proceedings of the Twenty-seventh International Conference on System Sciences, vol. 3, pp. 621–630.
• Maes, S., Tuyls, K. Vanschoenwinkel, B. Manderick, B.2002 Credit card fraud detection using bayesian and neural networks, Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies.
• Shen, A., Tong, R., Deng, Y. 2007. Application of classification models on credit card fraud detection, Service Systems and Service Management, International Conference on. pp. 1–4.
• Syeda, M., Zhang, Y.Q Pan, Y. 2002. Parallel granular neural networks for fast credit card fraud detection, Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 572–577.
• Henderson, K. Gallagher, B., Li, L. Akoglu, L. Eliassi-Rad, T., Tong, H. Faloutsos, C. 2011. It's who you now: graph mining using recursive structural features, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM , pp. 663–671.
• Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J. C. 2011. Data mining for credit card fraud: a comparative study, Decis. Support. Syst. 50 (3) 602–613.
• Duman, E., Elikucuk, I. 2013. Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization, in: I. Rojas, G. Joya, J. Cabestany (Eds.), Advances in Computational Intelligence. Vol. 7903 of Lecture Notes in Computer Science, Springer, Berlin Heidelberg, pp. 62–71.
• Srivastava, A. Kundu, A. Sural, S.Majumdar, A.K. 2008. Credit card fraud detection using hiddenmarkov model, IEEE Trans. Dependable Secure Comput. 5 (1) 37–48.
• Nader Mahmoudi, Ekrem Duman, 2015. Detecting credit card fraud by Modified Fisher Discriminant Analysis,Expert Systems with Applications, Volume 42, Issue 5, Pages 2510-2516.
• Neda Soltani Halvaiee, Mohammad Kazem Akbari, 2014. A novel model for credit card fraud detection using Artificial Immune Systems,Applied Soft Computing, Volume 24, Pages 40-49.
• Masoumeh Zareapoor, Pourya Shamsolmoali, 2015. Application of credit card fraud detection: Based on bagging ensemble classifier",Procedia Computer Science, Volume 48, Pages 679-685
• Jarrod West, Maumita Bhattacharya. 2016. Payment Card Fraud Detection Using Neural Network Committee and Clustering, Computers & Security, Volume 57, Pages 47-66.