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
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024-05
Agent
- Author (aut): Yang, Branden
- Thesis director: Osburn, Steven
- Committee member: Malpe, Adwith
- Committee member: Ahn, Gail-Joon
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Computer Science and Engineering Program