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
Index Terms—Spam, deep learning, word2vec, bag of word.
Samira. Douzi is with IPSS, Faculty of Science, University Mohammed Rabat, Morocco (e-mail: firstname.lastname@example.org).
Feda A. AlShahwan was with University of Surrey UK. She is now with the College of Technological Studies, Kuwait (e-mail: email@example.com).
Mouad. Lemoudden was with the IPSS, Faculty of Science, University Mohammed Rabat, Morocco. He is now with INRIA Rennes- Bretagne Atlantique, France (e-mail: firstname.lastname@example.org).
Bouabid El Ouahidi was with University of Caen-France. He is now with the IPSS, Faculty of Science, University Rabat, Morocco (e-mail email@example.com).
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).