A Learning Approach to Spam Detection based on Social Networks

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 much of the bandwidth of the Internet. Most spam filters in existence are based on the content of email one way or the other. While these anti-spam tools have proven very useful, they do not prevent the bandwidth 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 paper, 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. Moreover, we also explore various spam filtering and resisting possibilities.