Abstract: | Underground activity detection refers to the attempt to detect illegitimate usage of a communications network. Three methods to detect underground activity are presented. Firstly, a feed-forward neural network based on supervised learning is used to learn a discriminative function to classify subscribers using summary statistics. Secondly, Gaussian mixture model is used to model the probability density of subscribers` past behavior so that the probability of current behavior can be calculated to detect any abnormalities from the past behavior. Lastly, Bayesian networks are used to describe the statistics of a particular user and the statistics of different underground activity scenarios. The Bayesian networks can be used to infer the probability of fraud given the subscribers` behavior. The data features are derived from toll tickets. The experiments show that the methods detect over 82% of the fraudsters in our testing set without causing false alarms. |
