A study of the personalization of spam content using Facebook public information

Abstract

Millions of users per day are affected by unsolicited email campaigns. Spam filters are capable of detecting and avoiding an increasing number of messages, but researchers have quantified a response rate of a 0.006% [1], still significant to turn a considerable profit sending millions of emails, as the spammers do. While research directions are addressing topics such as better spam filters, or spam detection inside online social networks, in this paper we demonstrate that a classic spam model using online social network information can harvest a 7.62% of click-through rate. We collect email addresses from the Internet, complete email owner information using their public social network profile data, and analyze response of personalized spam sent to users according to their profile using a fake website. Finally we demonstrate the effectiveness of these profile-based emails to circumvent spam detection and we compare results between typical spam and personalized spam.