Synthesizing Test Data for Fraud Detection Systems

Authors:
Emilie Lundin Barse;Håkan Kvarnström;Erland Jonsson
Affiliations:
-;-;-
Venue:
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Year:
2003

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Abstract

This paper reports an experiment aimed at generatingsynthetic test data for fraud detection in an IP based video-on-demand service. The data generation verifies a methodologypreviously developed by the present authors [7] thatensures that important statistical properties of the authenticdata are preserved by using authentic normal data andfraud as a seed for generating synthetic data. This enablesus to create realistic behavior profiles for users and attackers.The data can also be used to train the fraud detectionsystem itself, thus creating the necessary adaptation of thesystem to a specific environment. Here we aim to verify theusability and applicability of the synthetic data, by usingthem to train a fraud detection system. The system is thenexposed to a set of authentic data to measure parameterssuch as detection capability and false alarm rate as well asto a corresponding set of synthetic data, and the results arecompared.