This paper describes a hybrid model for online fraud detection of the Video-on-Demand System as an E-commence application, which combines algorithms from the main two distinct viewpoints of the self, non-self theory and danger theory. Our artificial immune based algorithm includes the improved version of negative selection called Conserved Self Pattern Recognition Algorithm (CSPRA) and a recently established algorithm inspired by Danger Theory (DT) called Dendritic Cells Algorithm (DCA). The experimental results based on our Video-on-Demand case study demonstrate that the hybrid approach has a higher detection rate and lower false alarm when compared with the results achieved by only using CSPRA or DCA as individual algorithms.
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