A novel hybrid artificial immune inspired approach for online break-in fraud detection

Abstract

Abstract This paper proposes a new hybrid model for online fraud detection of the Video-on-Demand System, which is aimed to improve the current Risk Management Pipeline (RMP) by adding Artificial Immune System (AIS) based fraud detection for logging data. The AIS based model combines two artificial immune system algorithms with behavior based intrusion detection using Classification and Regression trees (CART). Immune inspired algorithms include 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 hybrid method based on stacking-bagging demonstrates higher detection rate lower false alarm, and handles high dimensional data set better when compared to the results achieved using only CSPRA, DCA, and CART.