Email Spam Detection Using Ensemble Learning

DOI

10.2991/978-94-6239-693-7_108How to use a DOI?

Keywords

Email Spam Detection; Ensemble Learning; TF-IDF; Machine Learning; Text Classification; Cybersecurity

Abstract

Email spam is one of the biggest problems of the modern communication technology, which has a significant negative impact on productivity and cybersecurity. Because of the exponential increase in Internet usage, spam emails have increased manyfold, making basic rule-based filtering approaches inefficient owing to their inability to update themselves according to changes in spam behaviour and the high resultant rate of false positives. While there were several approaches to employing machine learning algorithms for spam filtering, studies on spam email filtering using an ensemble learning approach have not been many so far. In this study, spam email filtering using ensemble learning is proposed to make it robust against any change in spamming patterns. Feature vector extraction is performed through the TF-IDF transformation of email texts, while other feature vectors considered in the study are sender details, email length, and the availability of URLs in an email. In this study, a soft voting ensemble classifier combines “ Naive Bayes, Logistic Regression And Support Vector Machine “ Algorithms for spam email classification. The experiment confirmed that the proposed approach is effective for efficient email spam filtering.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Sanjay
AU  - S. Sasikala
AU  - R. Anandha Sree
PY  - 2026
DA  - 2026/06/16
TI  - Email Spam Detection Using Ensemble Learning
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 1126
EP  - 1135
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_108
DO  - 10.2991/978-94-6239-693-7_108
ID  - Sanjay2026
ER  -