Phishpedia

Abstract

1. Introduction

Input: A URL and its screenshot Output: Phish/Benign, Phishing target

2. Objective

Official Implementation of "Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages" USENIX'21

This project demonstrates how Python, Computer Vision can be applied to a real-world Cybersecurity & Ethical Hacking problem.

4. Technology Stack

• Step 1: Enter Deep Object Detection Model, get predicted logos and inputs (inputs are not used for later prediction, just for explanation)
• Step 2: Enter Deep Siamese Model
• If Siamese report no target, Return Benign, None
• Else Siamese report a target, Return Phish, Phishing target

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/lindsey98/Phishpedia.git
cd Phishpedia

export KMP_DUPLICATE_LIB_OK=TRUE
  git clone https://github.com/lindsey98/Phishpedia.git
  cd Phishpedia
  pixi install
  chmod +x setup.sh
  ./setup.sh

git clone https://github.com/lindsey98/Phishpedia.git
  cd Phishpedia
  pixi install
  setup.bat

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add logging and alert notifications (email / Telegram)
• Write a threat model document for the tool
• Package it with Docker for safe lab testing

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which threat or attack does this project defend against?
2. What detection or protection technique is used and what are its limits?
3. How are false positives and false negatives handled?
4. Which cryptographic algorithms or security standards are involved?
5. What legal and ethical rules apply when testing a tool like this?

9. Source Code & License

This project is developed by lindsey98 and published on GitHub under the Creative Commons Zero v1.0 Universal. Please follow the license terms and credit the original author when you use or modify this code.