article

Review: A review of machine learning approaches to Spam filtering

Authors:

Thiago S. Guzella,

Walmir M. CaminhasAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 36, Issue 7

Pages 10206 - 10222

https://doi.org/10.1016/j.eswa.2009.02.037

Published: 01 September 2009 Publication History

Abstract

In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual- and image-based approaches. Instead of considering Spam filtering as a standard classification problem, we highlight the importance of considering specific characteristics of the problem, especially concept drift, in designing new filters. Two particularly important aspects not widely recognized in the literature are discussed: the difficulties in updating a classifier based on the bag-of-words representation and a major difference between two early naive Bayes models. Overall, we conclude that while important advancements have been made in the last years, several aspects remain to be explored, especially under more realistic evaluation settings.

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Cited By

Bevendorff JWiegmann MPotthast MStein B(2024)Product Spam on YouTube: A Case StudyProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638303(358-363)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638303
Jamal SWimmer HSarker I(2024)An improved transformer‐based model for detecting phishing, spam and ham emailsSecurity and Privacy10.1002/spy2.4027:5Online publication date: 24-Apr-2024
Li CYang XLiu BLiu WChen HElkind E(2023)Annealing genetic-based preposition substitution for text rubbish example generationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/569(5122-5130)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/569
Show More Cited By

Review: A review of machine learning approaches to Spam filtering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 36, Issue 7

September, 2009

597 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd © 2009.

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Pergamon Press, Inc.

United States

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Published: 01 September 2009

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Cited By

Bevendorff JWiegmann MPotthast MStein B(2024)Product Spam on YouTube: A Case StudyProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638303(358-363)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638303
Jamal SWimmer HSarker I(2024)An improved transformer‐based model for detecting phishing, spam and ham emailsSecurity and Privacy10.1002/spy2.4027:5Online publication date: 24-Apr-2024
Li CYang XLiu BLiu WChen HElkind E(2023)Annealing genetic-based preposition substitution for text rubbish example generationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/569(5122-5130)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/569
Nosrati VRahmani MJolfaei ASeifollahi S(2023)A Weak-Region Enhanced Bayesian Classification for Spam Content-Based FilteringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/351042022:3(1-18)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1145/3510420
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