Spam detection using hybrid of artificial neural network and genetic algorithm

Spam detection is a significant problem which is considered by many researchers by various developed strategies. In this study, the popular performance measure is a classification accuracy which deals with false positive, false negative and accuracy. These metrics were evaluated under applying two s...

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Main Author:	Arram, Anas W. A.
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/37019/5/AnasWAArramMFSKSM2013.pdf
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id	my-utm-ep.37019
record_format	uketd_dc
spelling	my-utm-ep.370192017-07-11T03:46:32Z Spam detection using hybrid of artificial neural network and genetic algorithm 2013-06 Arram, Anas W. A. QA75 Electronic computers. Computer science Spam detection is a significant problem which is considered by many researchers by various developed strategies. In this study, the popular performance measure is a classification accuracy which deals with false positive, false negative and accuracy. These metrics were evaluated under applying two supervised learning algorithms: hybrid of Artificial Neural Network (ANN) and Genetic Algorithm (GA), Support Vector Machine (SVM) based on classification of Email spam contents were evaluated and compared. In this study, a hybrid machine learning approach inspired by Artificial Neural Network (ANN) and Genetic Algorithm (GA) for effectively detect the spams. Comparisons have been done between classical ANN and Improved ANN-GA and between ANN-GA and SVM to show which algorithm has the best performance in spam detection. These algorithms were trained and tested on a 3 set of 4061 E-mail in which 1813 were spam and 2788 were nonspam. Results showed that the proposed ANN-GA technique gave better result compare to classical ANN and SVM techniques. The results from proposed ANNGA gave 93.71% accuracy, while classical ANN gave 92.08% accuracy and SVM technique gave the worst accuracy which was 79.82. The experimental result suggest that the effectiveness of proposed ANN-GA model is promising and this study provided a new method to efficiently train ANN for spam detection. 2013-06 Thesis http://eprints.utm.my/id/eprint/37019/ http://eprints.utm.my/id/eprint/37019/5/AnasWAArramMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70098?site_name=Restricted Repository 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 Arram, Anas W. A. Spam detection using hybrid of artificial neural network and genetic algorithm
description	Spam detection is a significant problem which is considered by many researchers by various developed strategies. In this study, the popular performance measure is a classification accuracy which deals with false positive, false negative and accuracy. These metrics were evaluated under applying two supervised learning algorithms: hybrid of Artificial Neural Network (ANN) and Genetic Algorithm (GA), Support Vector Machine (SVM) based on classification of Email spam contents were evaluated and compared. In this study, a hybrid machine learning approach inspired by Artificial Neural Network (ANN) and Genetic Algorithm (GA) for effectively detect the spams. Comparisons have been done between classical ANN and Improved ANN-GA and between ANN-GA and SVM to show which algorithm has the best performance in spam detection. These algorithms were trained and tested on a 3 set of 4061 E-mail in which 1813 were spam and 2788 were nonspam. Results showed that the proposed ANN-GA technique gave better result compare to classical ANN and SVM techniques. The results from proposed ANNGA gave 93.71% accuracy, while classical ANN gave 92.08% accuracy and SVM technique gave the worst accuracy which was 79.82. The experimental result suggest that the effectiveness of proposed ANN-GA model is promising and this study provided a new method to efficiently train ANN for spam detection.
format	Thesis
qualification_level	Master's degree
author	Arram, Anas W. A.
author_facet	Arram, Anas W. A.
author_sort	Arram, Anas W. A.
title	Spam detection using hybrid of artificial neural network and genetic algorithm
title_short	Spam detection using hybrid of artificial neural network and genetic algorithm
title_full	Spam detection using hybrid of artificial neural network and genetic algorithm
title_fullStr	Spam detection using hybrid of artificial neural network and genetic algorithm
title_full_unstemmed	Spam detection using hybrid of artificial neural network and genetic algorithm
title_sort	spam detection using hybrid of artificial neural network and genetic algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/37019/5/AnasWAArramMFSKSM2013.pdf
_version_	1747816491264245760

Spam detection using hybrid of artificial neural network and genetic algorithm

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