SIM Box Fraud Detection by Deep Learning System with ICA And Beta Divergence

We present a system for detecting fraud related to illegal transmission of telecommunications traffic of voice calls. This phenomenon, called SIM box, can be identified and limited by using Data Mining customer classification models. The results of these models can then be decomposed by Independent Component Analysis into latent source data from which destructive components can be identified. By identifying these components using Beta Divergence, eliminating them and performing the inverse transformation to Independent Component Analysis, we can improve prediction results. The process is organized in several layers, creating a unified Deep Learning System. We demonstrate the effectiveness of the approach in a practical experiment