Mining of identity theft stories to model and assess identity threat behaviors

Identity theft is an ever-present and ever-growing issue in our society. Identity theft, fraud and abuse are present and growing in every market sector. The data available to describe how these identity crimes are conducted and the consequences for victims is often recorded in stories and reports by the news press, fraud examiners and law enforcement. To translate and analyze these stories in this very unstructured format, this thesis first discusses the collection of identity theft data automatically using text mining techniques from the online news stories and reports on the topic of identity theft. The collected data are used to enrich the ITAP (Identity Threat Assessment and Prediction) Project repository under development at the Center for Identity at The University of Texas. Moreover, this thesis shows the statistics of common behaviors and resources used by identity thieves and fraudsters — identity attributes used to identify people, resources employed to conduct the identity crime, and patterns of identity criminal behavior. Analysis of these results should help researchers to better understand identity threat behaviors, offer people early warning signs and thwart future identity theft crimes.