Frameworks for fraud detection in mobile telecommunications networks

Fraud is costing the mobile communications industry millions of pounds a year. A rapid solution is needed to reduce fraudulent activity in analogue networks and preventative measures are required to protect GSM and later UMTS. A joint European projectAdvanced Security for Personal Communications Technologies'(ASPeCT), part of the ACTS programme 1, has been formed to research security issues in mobile communications networks. Part of this project is to investigate how Arti cial Intelligence can be used by a network operator to detect fraudulent activity in a real-time environment. We discuss ways to characterize a user's behaviour by computing user pro les over sequences of Toll Tickets. We show that with a neural network fraud detection system we can monitor user behaviour patterns through both di erential and absolute usage. A di erential analysis enables us to detect changes in behaviour associated with a mobile telephone which could indicate fraudulent usage after a theft or through cloning, more common under the TACS system. The GSM system is still relatively secure. The most common fraud is that of subscription fraud. We propose that our systems would detect this through absolute usage.