Feature extraction and classification of spam emails

Muhammad Ali Hassan; Nhamo Mtetwa

doi:10.1109/ISCMI.2018.8703222

Feature extraction and classification of spam emails

Muhammad Ali Hassan, Nhamo Mtetwa

Cyber Security and Networks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

267 Downloads (Pure)

Abstract

Emails are a popular and preferred way of written communication in our daily life. The problem with emails is spam. These spam emails are sent with different intentions, but advertisement and fraud are the main reasons. As being inexpensive to send, it causes many problems to the internet society. This paper discusses the use of different feature extraction methods coupled with two different supervised machine learning classifiers evaluated using four performance metrics on two publicly available spam email datasets for spam filtering. We highlight the importance of the correct coupling of feature extraction and classifier, and the merits of using two independent datasets.

Original language	English
Title of host publication	2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Publisher	IEEE
ISBN (Electronic)	9781728113012
DOIs	https://doi.org/10.1109/ISCMI.2018.8703222
Publication status	Published - 2 May 2019

Publication series

Name
ISSN (Print)	2640-0154
ISSN (Electronic)	2640-0146

Keywords

spam emails
machine intelligence
computing

Documents and Links

10.1109/ISCMI.2018.8703222

Hassan, M.A. & Mtetwa, N. (2019) Feature extraction and classification of spam emails'
Copyright © 2019 IEEE. This is a post-peer review, pre-copy edited version of the article and may differ slightly from the final published version of the work. This work is made available in line with the publisher policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Accepted author manuscript, 359 KB

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Cite this

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