Skip to main content

Categorization of Phishing Detection Features And Using the Feature Vectors to Classify Phishing Websites


Abstract Phishing is a form of online fraud where a spoofed website tries to gain access to user's sensitive information by tricking the user into believing that it is a benign website. There are several solutions to detect phishing attacks such as educating users, using blacklists or extracting phishing characteristics found to exist in phishing attacks. In this thesis, we analyze approaches that extract features from phishing websites and train classification models with extracted feature set to classify phishing websites. We create an exhaustive list of all features used in these approaches and categorize them into 6 broader categories and 33 finer categories. We extract 59 features from the URL, URL redirects, hosting domain (WHOIS and DNS r... (more)
Created Date 2017
Contributor Namasivayam, Bhuvana Lalitha (Author) / Bazzi, Rida (Advisor) / Zhao, Ziming (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Subject Computer science / classification / hosting domain / Neural networks / Phishing / predict phishing / website features
Type Masters Thesis
Extent 52 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  Full Text
544.7 KB application/pdf
Download Count: 1747

Description Dissertation/Thesis