Enhanced email spam filtering through combining similarity graphs
Anirban Dasgupta, Maxim Gurevich, Kunal Punera · 2011
Over the last decade Email Spam has evolved from being just an irritant to users to being truly dangerous. This has led web-mail providers and academic researchers to dedicate considerable resources towards tackling this problem [9, 21, 22, 24, 26]. However, we argue that some aspects of the spam filtering problem are not handled appropriately in existing work. Principal among these are adversarial spammer efforts -- spammers routinely tune their spam emails to bypass spam-filters, and contaminate ground truth via fake HAM/SPAM votes -- and the scale and sparsity of the problem, which essentially precludes learning with a very large set of parameters.