Anti-phishing model for phishing websites detection: using pruning decision tree

As a new form of malicious software, phishing websites appear frequently in recent years, Phishers use spoofed emails and fraudulent web sites to lure unsuspecting online users into giving up personal information. A lot of researches have been conducted to mitigate phishing websites. They used commo...

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Main Author:	M. Abunadi, Ahmed I.
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/35824/5/AhmedIMAbunadiMFSKSM2013.pdf
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id	my-utm-ep.35824
record_format	uketd_dc
spelling	my-utm-ep.358242021-07-19T08:22:51Z Anti-phishing model for phishing websites detection: using pruning decision tree 2013-06 M. Abunadi, Ahmed I. TK Electrical engineering. Electronics Nuclear engineering As a new form of malicious software, phishing websites appear frequently in recent years, Phishers use spoofed emails and fraudulent web sites to lure unsuspecting online users into giving up personal information. A lot of researches have been conducted to mitigate phishing websites. They used common phishing websites features in their investigation process. Some of these features do not have significant value on accuracy ratio which can affect the performance in terms of computation time. In this study, few new significant features for phishing websites are suggested. Meanwhile, Pruning Decision Tree is used for dectection of phishing websites because it is capable of balancing the computation time and accuracy ratio. Pessimistic Error Pruning is used as a pruning algorithm to prune the decision tree leafs without affecting the accuracy. This study also focused on categorization of phishing websites. The purpose of categorization process is to give significant and specific tips to increase the awareness level among users for each category. This study consists three main phases. First phase focused on dataset gathering, preprocessing, features extraction, dataset normalization and dataset division in order to make the dataset suitable for the classification process. Second phase focused on setup process of Decision Tree with Pessimistic Error Pruning technique. Third phase focueed on evaluation of results in terms of accuracy ratio, false positive and computation time. In addition, third phase focuesd on categorization of phishing websites. The result of this study shows an accuracy ratio of 99.12% before and after Pruning. That means the Pessimistic Error Pruning did not affact the accuracy ratio but it reduced the leafs of Decision tree to affect the computation time positively. 2013-06 Thesis http://eprints.utm.my/id/eprint/35824/ http://eprints.utm.my/id/eprint/35824/5/AhmedIMAbunadiMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70365?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	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering M. Abunadi, Ahmed I. Anti-phishing model for phishing websites detection: using pruning decision tree
description	As a new form of malicious software, phishing websites appear frequently in recent years, Phishers use spoofed emails and fraudulent web sites to lure unsuspecting online users into giving up personal information. A lot of researches have been conducted to mitigate phishing websites. They used common phishing websites features in their investigation process. Some of these features do not have significant value on accuracy ratio which can affect the performance in terms of computation time. In this study, few new significant features for phishing websites are suggested. Meanwhile, Pruning Decision Tree is used for dectection of phishing websites because it is capable of balancing the computation time and accuracy ratio. Pessimistic Error Pruning is used as a pruning algorithm to prune the decision tree leafs without affecting the accuracy. This study also focused on categorization of phishing websites. The purpose of categorization process is to give significant and specific tips to increase the awareness level among users for each category. This study consists three main phases. First phase focused on dataset gathering, preprocessing, features extraction, dataset normalization and dataset division in order to make the dataset suitable for the classification process. Second phase focused on setup process of Decision Tree with Pessimistic Error Pruning technique. Third phase focueed on evaluation of results in terms of accuracy ratio, false positive and computation time. In addition, third phase focuesd on categorization of phishing websites. The result of this study shows an accuracy ratio of 99.12% before and after Pruning. That means the Pessimistic Error Pruning did not affact the accuracy ratio but it reduced the leafs of Decision tree to affect the computation time positively.
format	Thesis
qualification_level	Master's degree
author	M. Abunadi, Ahmed I.
author_facet	M. Abunadi, Ahmed I.
author_sort	M. Abunadi, Ahmed I.
title	Anti-phishing model for phishing websites detection: using pruning decision tree
title_short	Anti-phishing model for phishing websites detection: using pruning decision tree
title_full	Anti-phishing model for phishing websites detection: using pruning decision tree
title_fullStr	Anti-phishing model for phishing websites detection: using pruning decision tree
title_full_unstemmed	Anti-phishing model for phishing websites detection: using pruning decision tree
title_sort	anti-phishing model for phishing websites detection: using pruning decision tree
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/35824/5/AhmedIMAbunadiMFSKSM2013.pdf
_version_	1747816364650790912

Anti-phishing model for phishing websites detection: using pruning decision tree

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