An analysis of the effectiveness of personalized spam using online social network public information

Abstract

Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. 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. 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 analyzed response of personalized spam sent to users according to their profile. Finally we demonstrate the effectiveness of these profile-based templates to circumvent spam detection.