Staff View: An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network

An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network

Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails...

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Main Authors:	Mohamad, M., Abdullah, E.F.H.S., Ghaleb, S.A.A., Ghanem, W.A.H.M.
Format:	Conference or Workshop Item
Language:	English
Published:	2021
Subjects:	HE Transportation and Communications QA Mathematics
Online Access:	http://eprints.unisza.edu.my/4749/1/FH03-FIK-21-51444.pdf http://eprints.unisza.edu.my/4749/
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id	my-unisza-ir.4749
record_format	eprints
spelling	my-unisza-ir.47492022-01-17T07:07:32Z http://eprints.unisza.edu.my/4749/ An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network Mohamad, M. Abdullah, E.F.H.S. Ghaleb, S.A.A. Ghanem, W.A.H.M. HE Transportation and Communications QA Mathematics Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails. Thus, the need for a spam email detector. The versatility and adaptability of the nature of spam influenced past innovations. However, previous techniques have been weakened. This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. To validate the performance of EGOA, executed on the spam email dataset are utilized, then the performance was relatively compared with popular search algorithms. The implementation demonstrates that EGOA introduces the best results with high accuracy of up to 96.09%. 2021 Conference or Workshop Item PeerReviewed text en http://eprints.unisza.edu.my/4749/1/FH03-FIK-21-51444.pdf Mohamad, M. and Abdullah, E.F.H.S. and Ghaleb, S.A.A. and Ghanem, W.A.H.M. (2021) An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network. In: 2nd International Conference on Advances in Cyber Security, 08-09 Dec 2020, Penang, Malaysia.
institution	Universiti Sultan Zainal Abidin
building	UNISZA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Sultan Zainal Abidin
content_source	UNISZA Institutional Repository
url_provider	https://eprints.unisza.edu.my/
language	English
topic	HE Transportation and Communications QA Mathematics
spellingShingle	HE Transportation and Communications QA Mathematics Mohamad, M. Abdullah, E.F.H.S. Ghaleb, S.A.A. Ghanem, W.A.H.M. An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
description	Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails. Thus, the need for a spam email detector. The versatility and adaptability of the nature of spam influenced past innovations. However, previous techniques have been weakened. This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. To validate the performance of EGOA, executed on the spam email dataset are utilized, then the performance was relatively compared with popular search algorithms. The implementation demonstrates that EGOA introduces the best results with high accuracy of up to 96.09%.
format	Conference or Workshop Item
author	Mohamad, M. Abdullah, E.F.H.S. Ghaleb, S.A.A. Ghanem, W.A.H.M.
author_facet	Mohamad, M. Abdullah, E.F.H.S. Ghaleb, S.A.A. Ghanem, W.A.H.M.
author_sort	Mohamad, M.
title	An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
title_short	An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
title_full	An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
title_fullStr	An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
title_full_unstemmed	An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
title_sort	integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
publishDate	2021
url	http://eprints.unisza.edu.my/4749/1/FH03-FIK-21-51444.pdf http://eprints.unisza.edu.my/4749/
_version_	1724079379511771136
score	13.211869

An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network

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