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)


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.


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


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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

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