  Spam mitigation can be broadly classified into two main approaches: a)
centralized security infrastructures that rely on a limited number of trusted
monitors to detect and report malicious traffic; and b) highly distributed
systems that leverage the experiences of multiple nodes within distinct trust
domains. The first approach offers limited threat coverage and slow response
times, and it is often proprietary. The second approach is not widely adopted,
partly due to the lack of guarantees regarding the trustworthiness of nodes
that comprise the system.
  Our proposal, SocialFilter, aims to achieve the trustworthiness of
centralized security services and the wide coverage, responsiveness and
inexpensiveness of large-scale collaborative spam mitigation. We propose a
large-scale distributed system that enables clients with no email
classification functionality to query the network on the behavior of a host. A
SocialFilter node builds trust for its peers by auditing their behavioral
reports and by leveraging the social network of SocialFilter administrators.
The node combines the confidence its peers have in their own reports and the
trust it places on its peers to derive the likelihood that a host is spamming.
  The simulation-based evaluation of our approach indicates its potential under
a real-world deployment: during a simulated spam campaign, SocialFilternodes
characterized 92% of spam bot connections with confidence greater than 50%,
while yielding no false positives
