Spam is becoming increasingly virulent as it makes use of images and
PDFs to evade content-based filters. To make matters worse, spammers
are sending spam from "fresh" machines every day, which makes it
difficult to maintain static blacklists of known bad senders.
To get a step ahead, the researchers have taken a different approach:
rather than filtering spam based on content or an ephemeral identity of
the sender (e.g., an IP address), the researchers have invented a new
technique called "behavioral blacklisting". Behavioral blacklisting
aims to learn and "fingerprint" spammers' sending patterns---for
example, the set of recipients a particular sender is targeting---and
blacklist senders based on their sending behavior, rather than a fixed
identity.
The researchers developed their first behavioral blacklisting technique
by applying Professor Vempala's novel spectral clustering algorithms,
which have also successfully been applied to other areas (e.g., Web
search).