PIPELINED FRAMEWORK FOR ANALYZING IDENTITY THEFT BEHAVIORS USING TEXT MINING

Abstract

In today’s era, identity theft is the most growing issue in society, businesses where the aspects of lives are digital. Identity theft is gateway crime, as criminals use others identities to steal the money for an Example credit card number, hack the social network authentication, etc. identity theft behaviors are identified by the various government investigative agency or media sources. These investigation committees are analyzing the identity theft records or news of identity thieves, which is in the form of recorded stories, report by media, rich site summary news feeds and hypertext markup language over the internet. At the National Institute of Justice “Identity theft becomes the defining crime of the information age, estimated amount of incident per year is 9 million or more. Over the past decade, the Federal Government and most states have passed legislation to impose criminal sanctions on identifying thieves. In this paper we present the pipe line system to better analysis of identity theft behavior by mine the identity theft stories using text mining technique .In this research news stories and records are collect from internet by using keywords related to identity theft, these records or stories are filter using text mining techniques and eliminates the duplicate information from the records and generate well formatted record in simple text format .By using this system can analyze pattern and resources used by thieves to commit identity theft.