Ego-Centric Approach For Predicting Fraudulent Collaboration In Telecommunication - Ab Raub, Rosmawati (2010)
Recently, there has been a surge of interest in social networks ever since the tragic event of September 11, 2001 attacks on The World Trade Center in the United States. E-mail traffic, disease transmission, criminal activity and communication network can all be modeled as social networks. Ego-centric is an approach used in social network analysis. In the social network parlance, the focused person is referred to as “ego” and his or her affiliate, friend or relative is known as “alters”. An egocentered network positions an individual at the center of a social network team for the person to traverse his or her relationships with other team members. Through social network analysis, enforcement officers can recognize how information flows through social ties, how people acquire information and resources and how cleavages and coalitions operate. In this thesis, based on social network theories and link analysis; a data mining technology, a social network analysis model is developed to facilitate in detecting fraudulent collaboration, after which an evaluation is then made on the performance of the developed model. This study aims to explore the usage of embedding social network analysis functions into fraudulent collaboration investigation in call details records. Two types of social network data collection approaches are discussed; (i) social network with centrality measures values and (ii) social network without centrality measures values, where the first approach is based on the previous research while the second is based on the current research experimented. Performance of the models produced by both approaches are measured based on a standard measurement. Performance is tested using statistical models which include Bayesian Network, Naïve Bayesian and Binary Logistic Regression Model is performed. These statistical models are used in order to prove and determine which model is the ‘best’ that can produce a better prediction of fraudulent collaboration. The outcome of this research is thought to be of help to any enforcement agency or relevant authority in its future operations or measures to detect fraudulent activity in social networks.