Electronic Fraud Detection for Video-on-Demand System Using Hybrid Immunology-Inspired Algorithms

  • Rentian Huang
  • Hissam Tawfik
  • Atulya Nagar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6209)


This paper proposes an improved version of current electronic fraud detection system by using logging data sets for Video-on-Demand system. Our approach is focused on applying Artificial Immune System based fraud detection algorithm for logging data information and accounting and billing purposes. Our hybrid approach combines algorithms from innate and adaptive parts of immune system, inspired by the Self non-self theory and the Danger theory. Our research proved the possibility of combining these to perform E-fraud detection. The experimental results demonstrated that hybrid approach has higher detection rate, lower false alarm when compared with the performances achieved by traditional classification algorithms such as Decision Tree, Support Vector Machines, and Radial Basis Function Neural Networks. Our approach also outperforms AIS approaches that use Dendritic Cell Algorithm, Conserved Self Pattern Recognition Algorithm, and Clonal Selection Algorithm individually.


Artificial Immune System Fraud Detection Video-on-Demand 


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  1. 1.
    Rozsnyai, S., Schiefer, J., Schatten, A.: Solutionarchitecture for detecting and preventing fraud in real time. In: Proceeding of Digital Informatino Management, ICDIM 2007, pp. 152–158 (2007)Google Scholar
  2. 2.
    Guo, T., Li, G.-Y.: Neural data mining for credit card fraud detection. In: Proceeding of International Conference on Machine Learning and Cybernetics, pp. 3630–3634 (2008)Google Scholar
  3. 3.
    Xu, J., Sung, A.H., Liu, Q.: Tree Based Behaviour Monitoring for Adaptive Fraud Detection. In: Proceeding of International Conference on Pattern Recognition, pp. 1208–1211 (2006)Google Scholar
  4. 4.
    Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 23–32 (2007)Google Scholar
  5. 5.
  6. 6.
    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
  7. 7.
    Greensmith, J., Twycross, J., Aickelin, U.: Dendritic cells for anomaly detection. In: Proc. of the Congress on Evolutionary Computation (CEC), pp. 664–671 (2006)Google Scholar
  8. 8.
    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
  9. 9.
    Huang, R., Tawfik, H., Nagar, A.: Licence Plate Character Recognition Using Artificial Immune Technique. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 823–832. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Keller, J.M., Gray, M.R., Givens Jr., J.A.: A Fuzzy K-Nearest Neighbor Algorithm. IEEE Transactions on Systems, Man, and Cybernetics 15(4), 580–585 (1985)Google Scholar
  11. 11.
    Moore, D.: Basic Practice of Statistics. W.H. Freeman, San Francisco (2006)Google Scholar
  12. 12.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2001) ISBN 0-471 05669-3zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

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