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International Symposium on Security in Computing and Communication

SSCC 2013: Security in Computing and Communications pp 293–302Cite as

Hybrid Approach for Improvising Credit Card Fraud Detection Based on Collective Animal Behaviour and SVM

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 377))

Abstract

The explosive growth of Information Technology in the last few decades has resulted in automation in every possible field. This has also led to electronic fund transfers and increased usage of credit cards and debit cards. Credit card fraud costs consumers and the financial industry billions of dollars annually. In this paper we propose a hybrid approach to credit card fraud detection, where a combination of supervised and unsupervised approaches was used to detect fraudulent transactions. This includes a behaviour based clustering approach where we use patterns from collective animal behaviours to detect the changes in the behaviour of credit card users to minimize the false positives. This approach also opens the avenue to predict the collective behaviours of highly organized crime groups involved in credit card fraud activities which as an option is not explored so far.

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References

  1. Pavia, J.M., Veres-Ferrer, E.J., Escura, G.F.: Credit Card Incidents and Control Systems. International Journal of Information Management, 501–503 (2012)

    Google Scholar 

  2. Bhattacharyya, S., Jha, S., Tharakunnel, K., Christopher Westland, J.: Data Mining For Credit Card Fraud: A Comparative Study. Decision Support Systems 50, 602–613 (2011)

    Article  Google Scholar 

  3. Krivko, M.: A Hybrid Model for Plastic Card Fraud Detection Systems. Expert Systems with Applications 37, 6070–6076 (2010)

    Article  Google Scholar 

  4. Duman, E., Hamdi Ozcelik, M.: Detecting Credit Card Fraud by Genetic Algorithm and Scatter Search. Expert Systems with Applications 38, 13057–13063 (2011)

    Article  Google Scholar 

  5. Panigrahi, S., Kundu, A., Sural, S., Majumdar, A.K.: Credit Card Fraud Detection: A Fusion Approach Using Dempster–Shafer Theory And Bayesian Learning. Information Fusion 10, 354–363 (2009)

    Article  Google Scholar 

  6. Vatsa, V., Sural, S., Majumdar, A.K.: A Game-Theoretic Approach to Credit Card Fraud Detection. Springer, Heidelberg (2005)

    Google Scholar 

  7. Elías, A., Ochoa-Zezzatti, A., Padilla, A., Ponce, J.: Outlier Analysis for Plastic Card Fraud Detection a Hybridized and Multi-Objective Approach. Springer, Heidelberg (2011)

    Google Scholar 

  8. Sańchez, D., Vila, M.A., Cerda, L., Serrano, J.M.: Association Rules Applied To Credit Card Fraud Detection. Expert Systems with Applications 36, 3630–3640 (2009)

    Article  Google Scholar 

  9. Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X.: The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. Decision Support Systems 50, 559–569 (2011)

    Article  Google Scholar 

  10. Gadi, M.F.A., Wang, X., do Lago, A.P.: Credit Card Fraud Detection with Artificial Immune System. Springer, Heidelberg (2008)

    Google Scholar 

  11. Jha, S., Guillen, M., Christopher Westland, J.: Employing Transaction Aggregation Strategy to Detect Credit Card Fraud. Expert Systems with Applications 39, 12650–12657 (2012)

    Article  Google Scholar 

  12. Quah, J.T.S., Sriganesh, M.: Real-Time Credit Card Fraud Detection Using Computational Intelligence. Expert Systems with Applications 35, 1721–1732 (2008)

    Article  Google Scholar 

  13. Sun, A., Lim, E.-P., Liu, Y.: On Strategies For Imbalanced Text Classification Using SVM: A Comparative Study. Decision Support Systems 48, 191–201 (2009)

    Article  Google Scholar 

  14. Zhang, H.-T., Chen, M.Z., Stan, G.-B., Zhou, T., Jan, M.: Maciejowski, Collective Behavior Coordination with Predictive Mechanisms. IEEE Circuits and Systems Magazine (2008)

    Google Scholar 

  15. Sumpter, D.J.T.: Phil. Trans. R. Soc. B. The Principles of Collective Animal Behaviour 361, 5–22 (2006)

    Google Scholar 

  16. Cuevas, E., Gonzalez, M., Zaldivar, D., Perez-Cisneros, M., Garcia, G.: An Algorithm for Global Optimization Inspired by Collective Animal Behaviour, vol. 2012, Article ID 638275, 24 pages. Hindawi Publishing Corporation Discrete Dynamics in Nature and Society (2012)

    Google Scholar 

  17. He, S., Wu, Q.H., Saunders, J.R.: Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior. IEEE Transactions on Evolutionary Computation 13(5) (October 2009)

    Google Scholar 

  18. Lu, J., Liu, J., Couzin, I.D., Levin, S.A.: Emerging Collective Behaviors of Animal Groups. In: Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27 (2008)

    Google Scholar 

  19. Puntheeranurak, S., Tsuji, H.: A Multi-Clustering Hybrid Recommender System. In: Seventh International Conference on Computer and Information Technology. IEEE (2007)

    Google Scholar 

  20. Phoungphol, P., Zhang, Y., Zha, Y., Srichandan, B.: Multiclass SVM with Ramp Loss for Imbalanced Data Classification. In: IEEE International Conference on Granular Computing (2012)

    Google Scholar 

  21. Tang, Y., Jin, B., Sun, Y., Zhang, Y.-Q.: Granular Support Vector Machines for Medical Binary Classification Problems. IEEE (2004)

    Google Scholar 

  22. Tang, Y., Zhang, Y.-Q., Chawla, N.V., Krasser, S.: SVMs Modeling for Highly Imbalanced Classification. IEEE (2009)

    Google Scholar 

  23. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification (2010)

    Google Scholar 

  24. Dheepa, V., Dhanapal, R.: Behavior Based Credit Card Fraud Detection Using Support Vector Machines (2012)

    Google Scholar 

  25. Dheepa, V., Dhanapal, R., Manjunath, G.: Fraud Detection in Imbalanced Datasets using Cost Based Learning (2012)

    Google Scholar 

  26. Yan-li, Z., Jia, Z.: Research on Data Preprocessing In Credit Card Consuming Behavior Mining. Energy Procedia 17, 638–643 (2012)

    Article  Google Scholar 

  27. Lemnaru, C., Cuibus, M., Bona, A., Alic, A., Potolea, R.: A Distributed Methodology for Imbalanced Classification Problems. In: 11th International Symposium on Parallel and Distributed Computing (2012)

    Google Scholar 

  28. Chen, R.-C., Luo, S.-T., Liang, X., Lee, V.C.S.: Personalized Approach Based on SVM and ANN for Detecting Credit Card Fraud. 0-7803-9422-4/05/$20.00 C2005. IEEE

    Google Scholar 

  29. Wei, X., Yuan, L.: An Optimized SVM Model for Detection of Fraudulent Online Credit Card Transactions. In: International Conference on Management of e-Commerce and e-Government (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Research and Development Centre, Bharathiar University, Coimbatore, 641 046, TamilNadu, India

    V. Dheepa

  2. Research Department of Computer Applications, Easwari Engineering College, Chennai, 600 089, TamilNadu, India

    R. Dhanapal

Authors
  1. V. Dheepa
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  2. R. Dhanapal
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Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Technopark Campus, 695581, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Department of Applied Computer Science, The University of Winnipeg, 515 Portage Avenue, R3B 2E9, Winnipeg, MB, Canada

    Pradeep K. Atrey

  3. Electrical and Computer Science Engineering Building, National Sun Yat-sen University, Kaohsiung, Taiwan R.O.C.

    Chun-I Fan

  4. Facultad de Informatica, Campus de Espinardo, s/n, 30.100, Murcia, Spain

    Gregorio Martinez Perez

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© 2013 Springer-Verlag Berlin Heidelberg

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Dheepa, V., Dhanapal, R. (2013). Hybrid Approach for Improvising Credit Card Fraud Detection Based on Collective Animal Behaviour and SVM. In: Thampi, S.M., Atrey, P.K., Fan, CI., Perez, G.M. (eds) Security in Computing and Communications. SSCC 2013. Communications in Computer and Information Science, vol 377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40576-1_29

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  • DOI: https://doi.org/10.1007/978-3-642-40576-1_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40575-4

  • Online ISBN: 978-3-642-40576-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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