• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace Home
  • Mühendislik Fakültesi
  • Bilgisayar Mühendisliği Bölümü
  • Bilgisayar Mühendisliği Bölümü Tez Koleksiyonu
  • View Item
  •   DSpace Home
  • Mühendislik Fakültesi
  • Bilgisayar Mühendisliği Bölümü
  • Bilgisayar Mühendisliği Bölümü Tez Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Utilization of Local and Global Image Descriptors for Phishing Web Page Identifıcation

View/Open
10324299yeni.pdf (1.897Mb)
Date
2020
Author
Eroğlu, Esra
xmlui.dri2xhtml.METS-1.0.item-emb
Acik erisim
xmlui.mirage2.itemSummaryView.MetaData
Show full item record
Abstract
In recent years, the use of the Internet has increased in all areas of life, thus many cyber- attacks have emerged. These attacks aim to steal users' private information such as passwords, credit cards. During phishing attacks, attackers have an attitude of deceiving users by creating copies of a web page that is known and frequently used by users. In this thesis, a new approach which can be a solution for detecting phishing attacks on web pages has been introduced. In the proposed approach, experiments have been conducted with local and global descriptors that have not been used before in the literature. In addition, "holistic" and "multi-level patch" approach was used to increase detection of attacks. The "holistic" approach referred to in these approaches is to process the image as a whole, while the "multi-level patch” approach is to separate the image into equal dimensions. The data set used in the evaluation phase of the proposed approach includes screenshots taken from websites of 14 different trademarks in total. This data set, with a total of 2852 samples, is "open set". The features obtained from the descriptors were then classified by support vector machine, random forest and XGBoost machine learning algorithms. According to the extensive test results, the best success rate is 90.38% with SIFT descriptor. This thesis suggests that the proposed approach may be effective in detecting possible counterfeiting attacks on web pages.
URI
http://hdl.handle.net/11655/22767
xmlui.mirage2.itemSummaryView.Collections
  • Bilgisayar Mühendisliği Bölümü Tez Koleksiyonu [184]
Hacettepe Üniversitesi Kütüphaneleri
Açık Erişim Birimi
Beytepe Kütüphanesi | Tel: (90 - 312) 297 6585-117 || Sağlık Bilimleri Kütüphanesi | Tel: (90 - 312) 305 1067
Bizi Takip Edebilirsiniz: Facebook | Twitter | Youtube | Instagram
Web sayfası:www.library.hacettepe.edu.tr | E-posta:openaccess@hacettepe.edu.tr
Sayfanın çıktısını almak için lütfen tıklayınız.
Contact Us | Send Feedback



DSpace software copyright © 2002-2016  DuraSpace
Theme by 
Atmire NV
 

 


DSpace@Hacettepe
huk openaire onayı
by OpenAIRE

About HUAES
Open Access PolicyGuidesSubcriptionsContact

livechat

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeDepartmentPublisherLanguageRightsxmlui.ArtifactBrowser.Navigation.browse_indexFundingxmlui.ArtifactBrowser.Navigation.browse_subtypeThis CollectionBy Issue DateAuthorsTitlesSubjectsTypeDepartmentPublisherLanguageRightsxmlui.ArtifactBrowser.Navigation.browse_indexFundingxmlui.ArtifactBrowser.Navigation.browse_subtype

My Account

LoginRegister

Statistics

View Usage Statistics

DSpace software copyright © 2002-2016  DuraSpace
Theme by 
Atmire NV