Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends

TL;DR

This paper provides a comprehensive review of machine learning techniques for email spam filtering, analyzing their effectiveness, evolution, and recent advancements in combating spam.

Contribution

It offers a detailed survey of content-based machine learning methods for spam filtering, highlighting recent trends and future directions.

Findings

Machine learning significantly improves spam detection accuracy.

Spam tactics are continuously evolving, requiring adaptive filtering techniques.

Recent developments show promising results in ML-based spam filtering effectiveness.

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.