Detecting Cellular Fraud Using Adaptive Prototypes

Yves Moreau

This paper discusses the current status of research on fraud detection undertaken as part of the European Commission-funded 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 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 no fraudulent examples are required for training. This is very relevant considering the currently secure nature of GSM where fraud scenarios, other than Subscription Fraud, have yet to manifest themselves. It is the aim of ASPeCT to be prepared for the would-be fraudster for both GSM and UMTS.