Hybrid approach for spam email detection

On this era, email is a convenient way to enable the user to communicate everywhere in the world which it has the internet. It is because of the economic and fast method of communication. The email message can send to the single user or distribute to the group. Majority of the users does not know th...

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Main Author:	Syed Hamed, Syed Mohd. Anwar Alhabshi
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/81161/1/SyedMohdAnwarMFC2018.pdf
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id	my-utm-ep.81161
record_format	uketd_dc
spelling	my-utm-ep.811612019-07-24T03:35:01Z Hybrid approach for spam email detection 2018-06 Syed Hamed, Syed Mohd. Anwar Alhabshi QA75 Electronic computers. Computer science On this era, email is a convenient way to enable the user to communicate everywhere in the world which it has the internet. It is because of the economic and fast method of communication. The email message can send to the single user or distribute to the group. Majority of the users does not know the life exclusive of e-mail. For this issue, it becomes an email as the medium of communication of a malicious person. This project aimed at Spam Email. This project concentrated on a hybrid approach namely Neural Network (NN) and Particle Swarm Optimization (PSO) designed to detect the spam emails. The comparisons between the hybrid approach for NN_PSO with GA algorithm and NN classifiers to show the best performance for spam detection. The Spambase used contains 1813 as spams (39.40%) and 2788 as non-spam (60.6%) implemented on these algorithms. The comparisons performance criteria based on accuracy, false positive, false negative, precision, recall and f-measure. The feature selection used by applying GA algorithm to reducing the redundant and irrelevant features. The performance of F-Measure shows that the hybrid NN_PSO, GA_NN and NN are 94.10%, 92.60% and 91.39% respectively. The results recommended using the hybrid of NN_PSO with GA algorithm for the best performance for spam email detection. 2018-06 Thesis http://eprints.utm.my/id/eprint/81161/ http://eprints.utm.my/id/eprint/81161/1/SyedMohdAnwarMFC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:119424 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Syed Hamed, Syed Mohd. Anwar Alhabshi Hybrid approach for spam email detection
description	On this era, email is a convenient way to enable the user to communicate everywhere in the world which it has the internet. It is because of the economic and fast method of communication. The email message can send to the single user or distribute to the group. Majority of the users does not know the life exclusive of e-mail. For this issue, it becomes an email as the medium of communication of a malicious person. This project aimed at Spam Email. This project concentrated on a hybrid approach namely Neural Network (NN) and Particle Swarm Optimization (PSO) designed to detect the spam emails. The comparisons between the hybrid approach for NN_PSO with GA algorithm and NN classifiers to show the best performance for spam detection. The Spambase used contains 1813 as spams (39.40%) and 2788 as non-spam (60.6%) implemented on these algorithms. The comparisons performance criteria based on accuracy, false positive, false negative, precision, recall and f-measure. The feature selection used by applying GA algorithm to reducing the redundant and irrelevant features. The performance of F-Measure shows that the hybrid NN_PSO, GA_NN and NN are 94.10%, 92.60% and 91.39% respectively. The results recommended using the hybrid of NN_PSO with GA algorithm for the best performance for spam email detection.
format	Thesis
qualification_level	Master's degree
author	Syed Hamed, Syed Mohd. Anwar Alhabshi
author_facet	Syed Hamed, Syed Mohd. Anwar Alhabshi
author_sort	Syed Hamed, Syed Mohd. Anwar Alhabshi
title	Hybrid approach for spam email detection
title_short	Hybrid approach for spam email detection
title_full	Hybrid approach for spam email detection
title_fullStr	Hybrid approach for spam email detection
title_full_unstemmed	Hybrid approach for spam email detection
title_sort	hybrid approach for spam email detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/81161/1/SyedMohdAnwarMFC2018.pdf
_version_	1747818311341572096

Hybrid approach for spam email detection

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