Classification Analysis for e-mail Spam using Machine Learning and Feed Forward Neural Network Approaches

DOI

10.2991/978-94-6463-471-6_7How to use a DOI?

Keywords

Email spam; Machine Learning; Deep Learning; Classification; Accuracy etc.

Abstract

In the present era, electronic communication plays an essential role in our daily lives. However, this convenience is accompanied by the persistent challenge of email spam, which inundates inboxes and poses a serious cybersecurity threat. Email spam remains a pervasive issue, with conventional spam filters often struggling to adapt to evolving spamming techniques. This paper aims to leverage machine learning advanced techniques to enhance the accuracy and efficiency of email spam classification. By employing state-of-the-art algorithms and models, the goal is to develop a robust and adaptable system capable of effectively identifying and filtering out spam emails. Several machine learning classifiers namely KNN, SVC, DT, NB, RF and Logistic Regression are applied. Later, a deep learning Feed Forward Neural Network model was applied and achieved good accuracy. The experiments’ outcome showed that the proposed deep learning gave good accuracy for email spam classification.

Copyright

© 2024 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  - Srinivasa Rao Dangeti
AU  - Dileep Kumar Kadali
AU  - Yesujyothi Yerramsetti
AU  - Ch Raja Rajeswari
AU  - D. Venkata Naga Raju
AU  - Srinath Ravuri
PY  - 2024
DA  - 2024/07/30
TI  - Classification Analysis for e-mail Spam using Machine Learning and Feed Forward Neural Network Approaches
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 66
EP  - 75
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_7
DO  - 10.2991/978-94-6463-471-6_7
ID  - Dangeti2024
ER  -