Mastering spam: a multifaceted approach with the Spamato spam filter system

Email is undoubtedly one of the most important applications used to communicate over the Internet. Unfortunately, the email service lacks a crucial security mechanism: It is possible to send emails to arbitrary people without revealing one’s own identity. Additionally, sending millions of emails costs virtually nothing. Hence over the past years, these characteristics have facilitated and even boosted the formation of a new business branch that advertises products and services via unsolicited bulk emails, better known as spam. Nowadays, spam makes up more than 50% of all emails and thus has become a major vexation of the Internet experience. Although this problem has been dealt with for a long time, only little success (measured on a global scale) has been achieved so far. Fighting spam is a cat and mouse game where spammers and anti-spammers regularly beat each other with sophisticated techniques of increasing complexity. While spammers try to bypass existing spam filters, anti-spammers seek to detect and block new spamming tricks as soon as they emerge. In this dissertation, we describe the Spamato spam filter system as a multifaceted approach to help regain a spam-free inbox. Since it is impossible to foresee future spam creation techniques, it is important to react quickly to their development. Spamato addresses this challenge in two ways. First, it has been designed to simplify the integration of multiple spam filters. By combining their different capabilities, a joint strike against spam promises the detection of more harmful messages than any individual solution could achieve. And second, we actively support collaborative spam filters that harness the collective knowledge of participating users. Such filters are therefore capable of learning about and eliminating new types of spam messages at an early stage. Hundreds of participants use Spamato everyday. The results presented in this dissertation provide insights into real-world operating environments rather than test bed scenarios often used in other projects. The results underpin our thesis that a concerted and collaborative filtering approach is an effective weapon in the arms race against spam.