 The model determines whether a new observation is consistent (or not) with respect to previous observations by applying, for instance, comparison rules, Hidden Markov models or Baye's rules. The profiles can be generated manually or automatically from profile templates. C 2009 by Taylor & Francis Group, LLC Towards a Fraud Detection Framework in VoIP Networks Neural networks have been of great interest for a lot of communities over the last few years, and are being successfully applied across a large range of prob- lem domains namely, finance, medicine, engineering and computer science. A neural network [14] is a computational model developed to be an imitation of the human brain with several computational units working in parallel. In other words, a neural network is a system of elementary decision units that can be adapted by training in order to recognize and classify arbitrary pat- terns. The interaction of a high number of elementary units makes it possible to learn arbitrary complex tasks. These units are connected by links which are assigned some weights representing the output of the unit. Some of the units are connected to the external environment and are dedicated as input or out- put units of the network. The network learns from examples, applying a func- tion to each unit's input giving its output. The weights are adjusted SO that the computational result conforms to the wanted result, given by the example. The network structure can either be feed forward or recurrent depending on whether the units are connected in a noncyclic manner or not. They can also be single layer or multi-layer networks. The latter adds hidden layers between the input and the output layer, to make more complex computations possible. A major drawback with neural networks is the construction time. A decision tree [9,10] takes as input an object or situation described by a set of properties (attributes) and outputs a yes/no decision. The internal nodes in the tree correspond to a test of the value of one of the attributes, and t