Full metadata
Title
PyAntiPhish: A Python-Based Machine Learning Detector of Phishing Websites and An Examination of Relevant URL-Based Features
Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
Date Created
2024-05
Contributors
- Yang, Branden (Author)
- Osburn, Steven (Thesis director)
- Malpe, Adwith (Committee member)
- Ahn, Gail-Joon (Committee member)
- Barrett, The Honors College (Contributor)
- Computer Science and Engineering Program (Contributor)
Topical Subject
Resource Type
Extent
58 pages
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2023-2024
Handle
https://hdl.handle.net/2286/R.2.N.191883
System Created
- 2024-03-22 05:21:40
System Modified
- 2024-03-28 12:20:42
- 8 months ago
Additional Formats