Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms

Spam detection is a significant problem that is considered by many researchers through various developed strategies. Creating a particular model to categorize the wide range of spam categories is complex; with understanding of spam types, which are always changing. In spam detection, low accuracy an...

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Main Author:	Ali Albshayreh, Ali Otman
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/53536/25/AliOtmanAliMFC2015.pdf
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id	my-utm-ep.53536
record_format	uketd_dc
spelling	my-utm-ep.535362020-07-19T07:40:50Z Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms 2015-01 Ali Albshayreh, Ali Otman QA75 Electronic computers. Computer science Spam detection is a significant problem that is considered by many researchers through various developed strategies. Creating a particular model to categorize the wide range of spam categories is complex; with understanding of spam types, which are always changing. In spam detection, low accuracy and the high false positive are substantial problems. So the trend to hire a global optimization algorithm is an appropriate way to resolve these problems due to its ability to create new solutions and non-compliance with local solutions. In this study, a hybrid machine learning approach inspired by Artificial Neural Network (ANN) and Differential Evolution (DE) are designed for effectively detect the spams. Comparisons have been done between ANN-DE with Genetic Algorithm (GA) and ANN-DE with InfoGain algorithm to show which approach has the best performance in spam detection. Spambase dataset of 4061 E-mail in which 1813 were spam (39.40%) and 2788 were non-spam (59.60%) were used to training and testing on these algorithms. The popular performance measure is a classification accuracy, which deals with false positive, false negative, accuracy, precision, and recall. These metrics were used for performance evaluation on the hybrid of ANN-DE with GA and InfoGain algorithm as feature selection algorithms. Performance of ANN-DE with GA and ANN-DE with InfoGain are compared. The experimental results show that the proposed hybrid technique of ANN-DE and GA gives better result with 93.81% accuracy compared to ANN-DE and InfoGain with 93.28% accuracy. The results recommend that the effectiveness of proposed ANN-DE with GA is promising and this study provided a new method to practically train ANN for spam detection. 2015-01 Thesis http://eprints.utm.my/id/eprint/53536/ http://eprints.utm.my/id/eprint/53536/25/AliOtmanAliMFC2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:84122 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 Ali Albshayreh, Ali Otman Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
description	Spam detection is a significant problem that is considered by many researchers through various developed strategies. Creating a particular model to categorize the wide range of spam categories is complex; with understanding of spam types, which are always changing. In spam detection, low accuracy and the high false positive are substantial problems. So the trend to hire a global optimization algorithm is an appropriate way to resolve these problems due to its ability to create new solutions and non-compliance with local solutions. In this study, a hybrid machine learning approach inspired by Artificial Neural Network (ANN) and Differential Evolution (DE) are designed for effectively detect the spams. Comparisons have been done between ANN-DE with Genetic Algorithm (GA) and ANN-DE with InfoGain algorithm to show which approach has the best performance in spam detection. Spambase dataset of 4061 E-mail in which 1813 were spam (39.40%) and 2788 were non-spam (59.60%) were used to training and testing on these algorithms. The popular performance measure is a classification accuracy, which deals with false positive, false negative, accuracy, precision, and recall. These metrics were used for performance evaluation on the hybrid of ANN-DE with GA and InfoGain algorithm as feature selection algorithms. Performance of ANN-DE with GA and ANN-DE with InfoGain are compared. The experimental results show that the proposed hybrid technique of ANN-DE and GA gives better result with 93.81% accuracy compared to ANN-DE and InfoGain with 93.28% accuracy. The results recommend that the effectiveness of proposed ANN-DE with GA is promising and this study provided a new method to practically train ANN for spam detection.
format	Thesis
qualification_level	Master's degree
author	Ali Albshayreh, Ali Otman
author_facet	Ali Albshayreh, Ali Otman
author_sort	Ali Albshayreh, Ali Otman
title	Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
title_short	Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
title_full	Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
title_fullStr	Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
title_full_unstemmed	Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
title_sort	spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2015
url	http://eprints.utm.my/id/eprint/53536/25/AliOtmanAliMFC2015.pdf
_version_	1747817575893434368

Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms

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