Data Mining Challenges for Electronic Safety: The Case of Fraudulent Intent Detection in E-Mails
Edoardo M. Airoldi, Bradley Malin · 2004
Online criminals have adapted traditional snail mail and door-to-door fraudulent schemes into electronic form. In-creasingly, such schemes target an individual’s personal e-mail, where they mingle among, and are masked by, hon-est communications. The targeting and conniving nature of these schemes are an infringement upon an individual’s personal privacy, as well as a threat to personal safety. In this paper, we introduce an array of challenges which are ripe for the attention of the data mining research commu-nity and are vastly different from those of combating the general problem of spam. We illustrate how state-of-the-art spam filtering systems fail to capture fraudulent intent hidden in the text of e-mails, but demonstrate how more robust systems can be engineered using existing data min-ing tools. We conclude by examining a specific scheme, the Nigerian 4-1-9 advance fee fraud scam, for which we de-sign a learning system capable of accurately identifying the fraudulent indent within an e-mail. Our system is appli-cable to fraud detection and can serve as a guide for law enforcement agencies in cyber-investigations. 1.