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Web application designed to help users determine if a given URL is malicious or not. Run by Finetuned BERT for classification task

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PhishHalt

PhishHalt is a web application designed to help users determine if a given URL is malicious or not. This project is built using JavaScript, HTML, CSS, and React.

Features

  • Input a URL to check if it is malicious.
  • Fast and efficient analysis.
  • Simple and user-friendly UI.

Technologies Used

  • JavaScript
  • React
  • HTML5
  • CSS3

Getting Started

To run PhishHalt on your local machine, follow the instructions below:

Prerequisites

Ensure you have the following installed on your system:

  • Node.js (v14 or higher)
  • npm (Node Package Manager)
  • PhishHalt Model (Email for access)

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/phishHalt.git
  2. Navigate to the server directory:

    cd server
  3. Navigate to the client directory:

    cd client
  4. Install dependencies:

    npm install
  5. Install model:

    After getting access to the model, add it to ../server

Running the Application

Once the dependencies are installed, you can start client using below command:

npm start

Next start the the development server using

uvicorn model:app --reload

This will start the application on localhost. By default, server should be available at http://localhost:8000/. By default, client should be available at http://localhost:3000/.

Usage

After starting the app, navigate to http://localhost:3000/ in browser to open the application.

Contributing

If you would like to contribute to PhishHalt, please follow these steps:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature-name).
  3. Make your changes.
  4. Commit your changes (git commit -m 'Add some feature').
  5. Push to the branch (git push origin feature-name).
  6. Open a Pull Request.

For any questions or feedback, feel free to reach out:

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Web application designed to help users determine if a given URL is malicious or not. Run by Finetuned BERT for classification task

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