Detecting Cellular Fraud Using Adaptive Prototypes.

Using a recurrent neural network technique, prototypes are uniformly distributed over Toll Tickets to form statistical behaviour proFdes covering both the short and long-term past to be prepared for the would-be fraudster for both GSM and UMTS.

Abstract

This paper discusses the current status of research on fraud detection undertaken as part of the European Commissionfunded ACTS ASPeCT (Advanced Security for Personal Communications Technologies) project, by Royal Holloway University of London. Using a recurrent neural network technique, we uniformly distribute prototypes over Toll Tickets, sampled from the U.K. network operator, Vodafone. The prototypes, which continue to adapt to cater for seasonal or long term trends, are used to classify incoming Toll Tickets to form statistical behaviour profiles covering both the short and long-term past. These behaviour profiles, maintained as probability distributions, comprise the input to a differential analysis utilising a measure known as the Hellinger distance[5] between them as an alarm criteria. Fine tuning the system to minimise the number of false alarms poses a significant task due to the low fraudulent/non fraudulent activity ratio. We benefit from using unsupervised learning in that...