Review on Email Spam Filtering Techniques

doi:10.23940/ijpe.21.02.p2.178190

Abstract

Abstract: A huge increase in the number of spam emails has led to the requirement for the evolution of more reliable and robust anti-spam techniques or filters that are utilized for preventing these emails (spam) from getting into inboxes. Machine Learning-based methods have been predominant and efficient in classifying emails as spam. This paper presents a broad review of successful and current machine learning-based methods that have been employed in email spam filtering. It also compares the strengths and limitations of current machine learning approaches that will guide researchers in efficiently dealing with the threat of spam in the future.

Key words: E-mail, Spam filtering, Machine Learning, Spam, Ham, Classification

Naina Nisar, Nitin Rakesh, and Megha Chhabra. Review on Email Spam Filtering Techniques [J]. Int J Performability Eng, 2021, 17(2): 178-190.

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