文章基本信息

标题：Network Intrusion Detection Using FP Tree Rules
本地全文：下载
作者：P Srinivasulu ; J Ranga RaoI Ramesh Babu
期刊名称：International Journal of Advanced Networking and Applications
电子版ISSN：0975-0290
出版年度：2009
卷号：1
期号：1
页码：30-39
出版社：Eswar Publications
摘要：In the faceless world of the Internet, o nline 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 have Terabyte level storage, human investigation to identify fraudulent transactions is very costly. This paper describes the auto matic 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
关键词：Fraud detection; FP tree; anomalies; adaptive mining; association mining