Spam detection with genetic optimized artificial immune system

Spam has become one of the most serious universal problems, which causes problems for almost all computer users. These problems such as lost productivity, wasting user’s time and occupying network bandwidth, causes a big problem for companies and organizations. This study presents a hybrid machine l...

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Bibliographic Details
Main Author:	Mehrsina, Alireza
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/33288/1/AlirezaMehrsinaMFSKSM2013.pdf
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id	my-utm-ep.33288
record_format	uketd_dc
spelling	my-utm-ep.332882017-09-13T03:36:07Z Spam detection with genetic optimized artificial immune system 2013-01 Mehrsina, Alireza QA75 Electronic computers. Computer science Spam has become one of the most serious universal problems, which causes problems for almost all computer users. These problems such as lost productivity, wasting user’s time and occupying network bandwidth, causes a big problem for companies and organizations. This study presents a hybrid machine learning approach inspired by the Artificial Immune System (AIS), and Genetic algorithm for effectively detect the Spams. The Clonal Selection Algorithm (CLONALG) is one of the famous implementations of the AIS, which is inspired by the clonal selection theory of acquired immunity, which has shown success on broad range of engineering problem domains. This algorithm is quietly similar to Genetic Algorithm in terms of architecture and behavior. In this study, Comparisons are drawn with AIS and GA-AIS classifiers and it is shown that the proposed system performs better results than the original AIS. 2013-01 Thesis http://eprints.utm.my/id/eprint/33288/ http://eprints.utm.my/id/eprint/33288/1/AlirezaMehrsinaMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69019?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mehrsina, Alireza Spam detection with genetic optimized artificial immune system
description	Spam has become one of the most serious universal problems, which causes problems for almost all computer users. These problems such as lost productivity, wasting user’s time and occupying network bandwidth, causes a big problem for companies and organizations. This study presents a hybrid machine learning approach inspired by the Artificial Immune System (AIS), and Genetic algorithm for effectively detect the Spams. The Clonal Selection Algorithm (CLONALG) is one of the famous implementations of the AIS, which is inspired by the clonal selection theory of acquired immunity, which has shown success on broad range of engineering problem domains. This algorithm is quietly similar to Genetic Algorithm in terms of architecture and behavior. In this study, Comparisons are drawn with AIS and GA-AIS classifiers and it is shown that the proposed system performs better results than the original AIS.
format	Thesis
qualification_level	Master's degree
author	Mehrsina, Alireza
author_facet	Mehrsina, Alireza
author_sort	Mehrsina, Alireza
title	Spam detection with genetic optimized artificial immune system
title_short	Spam detection with genetic optimized artificial immune system
title_full	Spam detection with genetic optimized artificial immune system
title_fullStr	Spam detection with genetic optimized artificial immune system
title_full_unstemmed	Spam detection with genetic optimized artificial immune system
title_sort	spam detection with genetic optimized artificial immune system
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2013
url	http://eprints.utm.my/id/eprint/33288/1/AlirezaMehrsinaMFSKSM2013.pdf
_version_	1747816125197975552

Spam detection with genetic optimized artificial immune system

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