Phishing Site Detection from a Web Developer's Perspective

dc.contributor.committeeMemberVerma, Rakesh M.
dc.contributor.committeeMemberLaszka, Aron
dc.contributor.committeeMemberKinsey, Denise M.
dc.creatorZhou, Xin 1992-
dc.creator.orcid0000-0002-6044-2105
dc.date.accessioned2019-12-21T00:42:49Z
dc.date.createdDecember 2019
dc.date.issued2019-12
dc.date.submittedDecember 2019
dc.date.updated2019-12-21T00:42:50Z
dc.description.abstractThe Internet has enabled unprecedented communication and new technologies. Concomitantly, it has brought the bane of phishing and exacerbated vulnerabilities. In this thesis, we propose a model to detect phishing webpages from a web developer's perspective. From this standpoint, we design 120 novel features based on content from a webpage, four time-based and two search-based novel features, plus we use 34 other content-based and 11 heuristic features to optimize the model. Moreover, we select Random Committee (Base learner: Random Tree) for our framework since it has the best performance after comparing with six other algorithms: Hellinger Distance Decision Tree, Support Vector Machine (SVM), Logistic Regression, J48, Naive Bayes, and Random Forest. In real-time experiments, the model achieved 99.4% precision and 98.3% Matthews correlation coefficient (MCC) with 0.1% false positive rate in 5-fold crossvalidation using the realistic scenario of an unbalanced dataset.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/5626
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectRandom Committee
dc.subjectMachine learning
dc.subjectPhishing site
dc.subjectPhishing
dc.titlePhishing Site Detection from a Web Developer's Perspective
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-12-01
local.embargo.terms2021-12-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

Files

Original bundle

Now showing 1 - 3 of 3
Loading...
Thumbnail Image
Name:
ZHOU-THESIS-2019.pdf
Size:
1.56 MB
Format:
Adobe Portable Document Format
No Thumbnail Available
Name:
Thesis - Xin (1).zip
Size:
1.82 MB
Format:
Unknown data format
No Thumbnail Available
Name:
Thesis - Xin.zip
Size:
1.82 MB
Format:
Unknown data format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.42 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.81 KB
Format:
Plain Text
Description: