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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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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
http://eprints.utm.my/id/eprint/33288/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69019?site_name=Restricted Repository
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spelling my.utm.332882017-09-13T03:36:07Z http://eprints.utm.my/id/eprint/33288/ Spam detection with genetic optimized artificial immune system 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/33288/1/AlirezaMehrsinaMFSKSM2013.pdf Mehrsina, Alireza (2013) Spam detection with genetic optimized artificial immune system. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69019?site_name=Restricted Repository
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2013
url http://eprints.utm.my/id/eprint/33288/1/AlirezaMehrsinaMFSKSM2013.pdf
http://eprints.utm.my/id/eprint/33288/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69019?site_name=Restricted Repository
_version_ 1643649286687162368
score 13.211869

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