A Comprehensive Review on Email Spam Classification with Machine Learning Methods

Authors

  • Prachi Bhatnagar  Research Scholar, Dept. of Computer Engineering, Sigma Institute of Engineering, Gujarat, India
  • Sheshang Degadwala  Associate Professor & Head of Department, Dept. of Computer Engineering, Sigma University, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT2361048

Keywords:

Email, Spam, Classification, Machine Learning, Algorithms, Review, Detection.

Abstract

This comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of research findings, methodologies, and performance metrics to provide a holistic perspective on the evolving landscape of spam detection. Emphasizing the pivotal role of machine learning in addressing the dynamic nature of spam, the review explores the strengths and limitations of popular algorithms such as Naive Bayes, Support Vector Machines, and neural networks. Additionally, it examines feature engineering, dataset characteristics, and evolving threats, offering insights into the challenges and opportunities within the field. With a focus on recent advancements and emerging trends, this review aims to guide researchers, practitioners, and developers in the ongoing pursuit of robust and adaptive email spam classification systems.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prachi Bhatnagar, Sheshang Degadwala, " A Comprehensive Review on Email Spam Classification with Machine Learning Methods" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.283-288, September-October-2023. Available at doi : https://doi.org/10.32628/CSEIT2361048