On the use of unsupervised techniques for fraud detection in VoIP networks

In traditional telecommunication networks, fraud is already a threat depriving telecom operators of huge amounts of money every year. With the migration from circuit-switched networks to packet-switched networks, it is expected that this situation will worsen. In this chapter, we present an unsupervised learning technique for classifying VoIP subscribers according to their potential involvement in fraud activities. This technique builds a signature for each subscriber to describe his or her typical behavior. Then the signature is used as a basis for comparison as it evolves over time. An implementation prototype of this technique was developed and assessed against real-life data delivered by a VoIP provider. The results were proven reasonable by comparing this technique to another unsupervised method, namely the Neural Network Self Organizing Map (NN-SOM).