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IJMLC 2020 Vol.10(2): 316-322 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.937

Hybrid Email Spam Detection Model Using Artificial Intelligence

Samira. Douzi, Feda A. AlShahwan, Mouad. Lemoudden, and Bouabid. El Ouahidi

Abstract—The growing volume of spam Emails has generated the need for a more precise anti-spam filter to detect unsolicited Emails. One of the most common representations used in spam filters is the Bag-of-Words (BOW). Although BOW is very effective in the classification of the emails, it has a number of weaknesses. In this paper, we present a hybrid approach to spam filtering based on the Neural Network model Paragraph Vector-Distributed Memory (PV-DM). We use PV-DM to build up a compact representation of the context of an email and also of its pertinent features. This methodology represents a more comprehensive filter for classifying Emails. Furthermore, we have conducted an empirical experiment using Enron spam and Ling spam datasets, the results of which indicate that our proposed filter outperforms the PV-DM and the BOW email classification methods.

Index Terms—Spam, deep learning, word2vec, bag of word.

Samira. Douzi is with IPSS, Faculty of Science, University Mohammed Rabat, Morocco (e-mail: samiradouzi8@gmail.com).
Feda A. AlShahwan was with University of Surrey UK. She is now with the College of Technological Studies, Kuwait (e-mail: fa.alshahwan@paaet.edu.kw).
Mouad. Lemoudden was with the IPSS, Faculty of Science, University Mohammed Rabat, Morocco. He is now with INRIA Rennes- Bretagne Atlantique, France (e-mail: mouad.lemoudden@gmail.com).
Bouabid El Ouahidi was with University of Caen-France. He is now with the IPSS, Faculty of Science, University Rabat, Morocco (e-mail bouabid.ouahidi@gmail.com).

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Cite: Samira. Douzi, Feda A. AlShahwan, Mouad. Lemoudden, and Bouabid. El Ouahidi, "Hybrid Email Spam Detection Model Using Artificial Intelligence," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 316-322, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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