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https://github.com/kiooku/anti-phishing
Phishing website detection using random forest
https://github.com/kiooku/anti-phishing
Last synced: 24 days ago
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Phishing website detection using random forest
- Host: GitHub
- URL: https://github.com/kiooku/anti-phishing
- Owner: Kiooku
- Created: 2023-11-01T15:10:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-01T17:21:38.000Z (over 1 year ago)
- Last Synced: 2024-11-11T11:31:03.421Z (3 months ago)
- Language: Jupyter Notebook
- Size: 2.8 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Anti-Phishing
This is *coursework* for the *big data analytics* class, that **identifies whether a website is legitimate or a phishing site** using **random forest**.
The aim was to learn as much as possible about supervised machine learning, and in the end to create a jupyter notebook on the topic of our choice *(phishing detection in my case)*.
## Overview
The coursework is on a jupyter notebook *(`coursework_phishing_website_detection.ipynb`)* which is 100% reproducible and explains my thinking step by step.
There are several stages in this coursework:
1. Research & Data Exploration
1. Dataset presentation
2. Related Work & Data Exploration
3. Data Pre-processing
2. Modelling/ Classification
3. Solution Improvement
Key words:
- Random Forest Classification
- Gradient Boosted Trees
- Cross-validation
- Randomized Search
- Grid Search
- Fully Homomorphic Encryption Machine Learning
As a bonus, I decided to create a [streamlit](https://streamlit.io/) application to simulate a real-world implementation of an anti-phishing solution based on machine learning.
> **Note** To run the streamlit app that allow you to determine if it's a phishing or legitimate website based on URL do the following command: `streamlit run phishing_website_detection_app.py`
https://github.com/Kiooku/Anti-Phishing/assets/33032066/48abf84a-85a0-4dd1-91cd-63c71e5f55b7