Towards an Artificial Immune System for Online Fraud Detection

  • Rentian Huang
  • Hissam Tawfik
  • Atulya Nagar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6825)

Abstract

Fraud is one of the largest growing problems experienced by many organizations as well as affecting the general public. Over the past decade the use of global communications and the Internet for conducting business has increased in popularity, which has been facing the fraud threat. This paper proposes an immune inspired adaptive online fraud detection system to counter this threat. This proposed system has two layers: the innate layer that implements the idea of Dendritic Cell Analogy (DCA), and the adaptive layer that implements the Dynamic Clonal Selection Algorithm (DCSA) and the Receptor Density Algorithm (RDA). The experimental results demonstrate that our proposed hybrid approach combining innate and adaptive layers of immune system achieves the highest detection rate and the lowest false alarm rate compared with the DCA, DCSA, and RDA algorithms for Video-on-Demand system.

Keywords

Fraud Detection Receptor Density Algorithm Dynamic Clonal Selection Algorithm Dendritic Cells Analogy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Satti, M.M., Gamer, B.J., Nagrial, M.H.: Information security standard for E businesses. In: Proc. 8th Intl. Conf. on Commun. System, vol. 2, pp. 641–645 (2008)Google Scholar
  2. 2.
    Mashima, D., Ahamad, M.: Using Identity Credential Usage Logs to Detect Anomalous Service Accesses. DIM, Chicago (2009)CrossRefGoogle Scholar
  3. 3.
    Williams, P.D., Anchor, K.P., Bebo, J.L., Gunsch, G.H., Lamont, G.D.: CDIS: Towards a Computer Immune System for Detecting Network Intrusions. In: Lee, W., Mé, L., Wespi, A. (eds.) RAID 2001. LNCS, vol. 2212, pp. 117–133. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Greensmith, J., Twycross, J., Aickelin, U.: Dendritic cells for anomaly detection. In: Harper, R., Rauterberg, M., Combetto, M. (eds.) ICEC 2006. LNCS, vol. 4161, pp. 664–671. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Hofmeyr, S.A., Forrest, S.: Architecture for an artificial immune system. Evolutionary Computation 7(1), 45–68 (2000)Google Scholar
  6. 6.
    Cortés, P., García, J.M., Onieva, L., Muñuzuri, J., Guadix, J.: Viral System to Solve Optimization Problems: An Immune-Inspired Computational Intelligence Approach. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 83–94. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Castro, P.A.D., Zuben, F.J.Von.: Mobais: a Bayesian Artificial Immune System for Multi-Objective Optimization. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 49–59. Springer, Heidelberg (2008)Google Scholar
  8. 8.
    Oates, R., Greensmith, J., Aickelin, U., Garibaldi, J., Kendall, G.: The application of a dendritic cell algorithm to a robotic classifier. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 204–215. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Qiang, C., Xiangpin, L., Chuang, X.: A Model for Detection and Diagnosis of Fault Based on Artificial Immune Theory. Journal of Southern Institute of Memallurgy 126(3) (2005)Google Scholar
  10. 10.
    Tuo, J., Ren, S., Liu, W., Li, X., Li, B., Lei, L.: Artificial Immune System for Fraud Detection. In: proceeding of IEEE International Conference on Systems, Man and Cybernetics, pp. 1407–1411 (2004)Google Scholar
  11. 11.
    Huang, R., Tawfik, H., NagaRentian, A.: Artificial Dendritic Cells Algorithm for Online Break-in Fraud Detection. In: Proceeding of International Conference on Developments in eSystems Engineering, Abu Dhabi, UAE, pp. 181–189 (2009)Google Scholar
  12. 12.
    Huang, R., Tawfik, H., Nagar, A.: Electronic Fraud Detection for Video-on-Demand System Using Hybrid Immunology-Inspired Algorithms. In: Proceeding of The 9th International Conference on Artificial Immune Systems, Edinburgh, UK, pp. 290–303 (2010)Google Scholar
  13. 13.
    Gadi, M.F.A., Wang, X., do Lago, A.P.: Credit Card Fraud Detection with Artificial Immune System. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 119–131. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Brabazon, A., Cahill, J., Keenan, P., Walsh, D.: Identifying online credit card fraud using artificial immune systems. In: Yang, H.S., Malaka, R., Hoshino, J., Han, J.H. (eds.) ICEC 2010. LNCS, vol. 6243. Springer, Heidelberg (2010)Google Scholar
  15. 15.
    Timmis, J., Tyrrell, A., Mokhtar, M., Ismail, A.R., owen, N., Bi, R.: An Artificial Immune System for Robot Organisms. Adaptive Control Mechanisms, 279–302 (2010)Google Scholar
  16. 16.
    De Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. In: Proceedings of GECCO 2000. Workshop on Artificial Immune Systems and Their Applications. 36–37 (2000)Google Scholar
  17. 17.
    Yu, S., Dasgupta, D.: Conserved Self Pattern Recognition Algorithm. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 279–290. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Owens, N., Greensted, A., Timmis, J., Tyrrell, A.: T cell receptor signalling inspired kernel density estimation and anomaly detection. In: Andrews, P.S., Timmis, J., Owens, N.D.L., Aickelin, U., Hart, E., Hone, A., Tyrrell, A.M. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 122–135. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Secker.,Freitas, A., Timmis, J.: AISEC: An Artificial Immune System for Email Classification. In: Proceedings of the Congress on Evolutionary Computation, vol. 2003, pp. 131–139. IEEE, Canberra (2005)Google Scholar
  20. 20.
    Lundin, E., Kvarnström, H., Jonsson, E.: A synthetic fraud data generation methodology. In: Deng, R.H., Qing, S., Bao, F., Zhou, J. (eds.) ICICS 2002. LNCS, vol. 2513, pp. 265–277. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rentian Huang
    • 1
  • Hissam Tawfik
    • 1
  • Atulya Nagar
    • 1
  1. 1.Faculty of Business and Computer SciencesLiverpool Hope UniversityLiverpoolUnited Kingdom

Personalised recommendations