Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/prmditya/phish-guard
URL based phising detector website using machine learning
https://github.com/prmditya/phish-guard
ai decision-trees flask knn-classification logistic-regression naive-bayes-classifier python random-forest-classifier smartsystem
Last synced: 16 days ago
JSON representation
URL based phising detector website using machine learning
- Host: GitHub
- URL: https://github.com/prmditya/phish-guard
- Owner: prmditya
- License: mit
- Created: 2024-11-09T02:33:00.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-08T04:19:57.000Z (2 months ago)
- Last Synced: 2024-12-08T05:18:53.520Z (2 months ago)
- Topics: ai, decision-trees, flask, knn-classification, logistic-regression, naive-bayes-classifier, python, random-forest-classifier, smartsystem
- Language: Jupyter Notebook
- Homepage:
- Size: 52.4 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
AI Powered Website for identifying phishing website from URL.
## **Table of Contents**
- [**Table of Contents**](#table-of-contents)
- [**About the Project**](#about-the-project)
- [**Features**](#features)
- [**Technologies Used**](#technologies-used)
- [**Installation**](#installation)
- [Steps](#steps)
- [**Usage**](#usage)
- [**License**](#license)
- [**Contributing**](#contributing)
- [**Acknowledgments**](#acknowledgments)
---
## **About the Project**
Phish Guard is a website developed using machine learning to detect phishing websites based on the provided URL. This project was created as part of the final project assignment for the Intelligent Systems course.
---
## **Features**
Highlight the key features of this project.
- **URL Phishing Detection**: Detects whether a given URL is linked to a phishing website using machine learning models.
- **Real-time Analysis**: Analyzes URLs in real-time and provides instant results on the legitimacy of the website.
- **User-Friendly Interface**: Offers an easy-to-use interface for users to input URLs and view detection results.
---
## **Technologies Used**
List the technologies, tools, or frameworks that being used in the project.
- [Flask](https://flask.palletsprojects.com/en/stable/) - A lightweight web framework for building web applications in Python.
- [Scikit-Learn](https://scikit-learn.org/stable/) - A machine learning library for Python, used for building and evaluating the model.
- [Pandas](https://pandas.pydata.org/docs/getting_started/index.html) - A data manipulation and analysis library used to handle datasets.
- [Numpy](https://numpy.org/) - A library for numerical computing, used for working with arrays and matrices in the project.
- [Requests](https://requests.readthedocs.io/en/latest/) - A library for making HTTP requests, if you're fetching website data for analysis.
- [Google Colab](https://colab.research.google.com/) - A cloud-based platform for running Jupyter notebooks, used for developing and training the machine learning model.
---
## **Installation**
This section explain about how to Install or clone and run the project.
### Steps
1. Clone the repository:
```bash
git clone https://github.com/prmditya/phish-guard.git
```
2. Navigate to the project directory:
```bash
cd phish-guard
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the application:
```bash
python run.py
```
---
## **Usage**
1. Enter a URL into the input form.
2. Click the "Analyze" button.
3. The result of the identification will appear at the bottom of the page.
---
## **License**
This project is licensed under the [MIT License](LICENSE).
---
## **Contributing**
Contributions are welcome! Follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-name`).
3. Commit your changes (`git commit -m "Add feature"`).
4. Push to the branch (`git push origin feature-name`).
5. Open a pull request.
---
## **Acknowledgments**
I would like to express my gratitude to the following resources and individuals who contributed to this project:
- The inspiration for the project came from this [Kaggle notebook on URL classification](https://www.kaggle.com/code/busrabetulcavusoglu/urls-classification).
- I would also like to thank my friends, [Bizzati Hanif R.F](https://github.com/Bizzati), [M. Yusuf Ramadhan](), and [Fatyatulhaqq Diando N](), for their support and assistance throughout the development of this project.
---