Technologies to Defeat Fraudulent Schemes Related to Email Requests

Online criminals have adapted traditional snail mail and door-to-door fraudulent schemes into electronic form. Increasingly, such schemes target an individual's personal e- mail, where they mingle among, and are masked by, honest 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. We argue that 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 starting from existing AI tools. We illustrate how to design a learning system capable of accurately identifying the fraudulent indent within an e-mail in order to tackle, for example, the advance fee fraud scam. Further, we propose data structures, as well as statistical tests for them, which capture evolutionary patterns within e-mails that are not likely to be due to chance. Last, our system can serve as a guide for law enforcement agencies in cyber-investigations.