Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends
This is an incremental review paper for researchers and practitioners in email security, summarizing existing methods without introducing new techniques.
The paper provides a comprehensive review of content-based email spam filtering techniques, focusing on machine learning methods and their effectiveness, and concludes by measuring their impact and exploring recent developments.
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.