FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR

The trends of cybercrime through fake website make people try to design fake website detection system. Using a Statistical Learning Theory, we propose a mathematical model for detecting fake websites based on Plug-in Classier with Bayesian Statistics and Support Vector Machine Linear methods. Thi...

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Main Author: Anisah
Format: Theses
Language:Indonesia
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Online Access:https://digilib.itb.ac.id/gdl/view/33867
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:33867
spelling id-itb.:338672019-01-30T15:06:21ZFAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR Anisah Ilmu alam dan matematika Indonesia Theses Statistical Learning Theory, Plug-in Classier, Bayesian Statistics, Classication, Fake Website. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/33867 The trends of cybercrime through fake website make people try to design fake website detection system. Using a Statistical Learning Theory, we propose a mathematical model for detecting fake websites based on Plug-in Classier with Bayesian Statistics and Support Vector Machine Linear methods. This research uses fraud cue as training data which consist of internal links, webpage levels, and screenshoot of webpage header. These two methods applied to detect a number of legitimate and fake websites. The result shows that the Support Vector Machine Linear model has better website detection than the Plug-in Classier model. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
topic Ilmu alam dan matematika
spellingShingle Ilmu alam dan matematika
Anisah
FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR
description The trends of cybercrime through fake website make people try to design fake website detection system. Using a Statistical Learning Theory, we propose a mathematical model for detecting fake websites based on Plug-in Classier with Bayesian Statistics and Support Vector Machine Linear methods. This research uses fraud cue as training data which consist of internal links, webpage levels, and screenshoot of webpage header. These two methods applied to detect a number of legitimate and fake websites. The result shows that the Support Vector Machine Linear model has better website detection than the Plug-in Classier model.
format Theses
author Anisah
author_facet Anisah
author_sort Anisah
title FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR
title_short FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR
title_full FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR
title_fullStr FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR
title_full_unstemmed FAKE WEBSITE DETECTION USING SUPPORT VECTOR MACHINE LINEAR
title_sort fake website detection using support vector machine linear
url https://digilib.itb.ac.id/gdl/view/33867
_version_ 1821996615838203904