A study of the personalization of spam content using Facebook public information
Enaitz Ezpeleta, Urko Zurutuza, José María Gómez Hidalgo · Logic Journal of IGPL · 2016
Millions of users per day are affected by unsolicited e-mail 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 (OSNs), in this article, we demonstrate that a classic spam model using OSN 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 analyse 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.