||The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not only does it annoy users, but it also consumes a lot of the Internet’s bandwidth. This thesis studies the problem of spam and provides a survey to the existing and proposed preventive and technological anti-spam measures. Most spam filters in existence are based on content analysis. While these anti-spam tools have proven useful, they do not protect bandwidths from being wasted and spammers are learning to bypass them via clever manipulation of the spam content. A very different approach to spam detection is based on the behavior of email senders. In this thesis, we propose a learning approach to spam sender detection based on features extracted from social networks constructed from email exchange logs. Legitimacy scores are assigned to senders based on their likelihood of being a legitimate sender. Three potential spam mitigation schemes are also explored.