Abstract 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.
Bhowmick and Hazarika REF presented a broad review of some of the popular content-based e-mail spam filtering methods.
Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends
ArXiv
ArXiv
Mathematics Computer Science