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Hybrid Email Spam Detection Model Using Artificial Intelligence

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

Authors

Samira Douzi

Feda A. AlShahwan

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.

Journal Article Type Article
Publication Date 2020-02
Deposit Date Feb 28, 2023
Publicly Available Date Feb 28, 2023
Journal International Journal of Machine Learning and Computing
Peer Reviewed Peer Reviewed
Volume 10
Issue 2
Pages 316-322
DOI https://doi.org/10.18178/ijmlc.2020.10.2.937
Keywords Spam, deep learning, word2vec, bag of word

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