Open Access

ARTICLE

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Sanaa A. A. Ghaleb^1,3,4, Mumtazimah Mohamad¹, Syed Abdullah Fadzli¹, Waheed A.H.M. Ghanem^2,3,4,*
1 Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kuala Terengganu, 22200, Malaysia
2 Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu, 21030, Malaysia
3 Faculty of Engineering, University of Aden, Aden, Yemen
4 Faculty of Education (Aden-Saber), University of Aden, Aden, Yemen
* Corresponding Author: Waheed A.H.M. Ghanem. Email:

Computers, Materials & Continua 2022, 71(3), 4749-4766. https://doi.org/10.32604/cmc.2022.020472

Received 25 May 2021; Accepted 30 August 2021; Issue published 14 January 2022

Abstract

Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and GOA produce an artificial neural network (ANN) model, referred to (GOAMLP). Two corpora are applied Spam Base and UK-2011 Web spam for this approach. Finally, the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

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Keywords

Grasshopper optimization algorithm; multilayer perceptron; artificial neural network; spam detection approach

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