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