  We present a comprehensive review of the most effective content-based e-mail
spam filtering techniques. We focus primarily on Machine Learning-based spam
filters and their variants, and report on a broad review ranging from surveying
the relevant ideas, efforts, effectiveness, and the current progress. The
initial exposition of the background examines the basics of e-mail spam
filtering, the evolving nature of spam, spammers playing cat-and-mouse with
e-mail service providers (ESPs), and the Machine Learning front in fighting
spam. We conclude by measuring the impact of Machine Learning-based filters and
explore the promising offshoots of latest developments.
