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.
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Huang, R., Tawfik, H., Nagar, A. (2011). Towards an Artificial Immune System for Online Fraud Detection. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_33
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DOI: https://doi.org/10.1007/978-3-642-22371-6_33
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