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UNDERGROUND ACTIVITY DETECTION IN COMMUNICATIONS NETWORKS USING NEURAL NETWORKS


BĂLĂŞOIU LEONARD
UNIVERSITY OF BACĂU

Issue:

MOCM, Number 10, Volume II

Section:

Issue No. 10 - Volume II (2004)

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.

Keywords:

[n/a]

Code [ID]:

MOCM200410V02S01A0002 [0000679]


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