  In the faceless world of the Internet,online fraud is one of the greatest
reasons of loss for web merchants.Advanced solutions are needed to protect e
businesses from the constant problems of fraud.Many popular fraud detection
algorithms require supervised training,which needs human intervention to
prepare training cases.Since it is quite often for an online transaction
database to ha e Terabyte level storage,human investigation to identify
fraudulent transactions is very costly.This paper describes the automatic
design of user profiling method for the purpose of fraud detection.We use a FP
(Frequent Pattern) Tree rule learning algorithm to adaptively profile
legitimate customer behavior in a transaction database.Then the incoming
transactions are compared against the user profile to uncover the anomalies The
anomaly outputs are used as input to an accumulation system for combining
evidence to generate high confidence fraud alert value. Favorable experimental
results are presented.

Networking and Internet Architecture