The Phishing URL Classification System is a machine learning-based approach to identify and classify phishing URLs using various URL features. This project aims to enhance online security by dynamically analyzing URLs to distinguish between legitimate and malicious web addresses.
In the digital era, cybersecurity is of paramount importance. Phishing attacks, in particular, pose a significant threat by impersonating trustworthy entities to steal sensitive information from unsuspecting users. The Phishing URL Classification System leverages machine learning algorithms to automatically detect and classify phishing URLs, providing users with real-time warnings about potential threats.
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URL Feature Analysis: The system extracts a diverse range of URL features, including domain length, presence of special characters, protocol type (HTTP/HTTPS), and more.
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Machine Learning Model: Trained on a comprehensive dataset, the system utilizes a Random Forest classifier to differentiate between legitimate and phishing URLs.
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Real-Time Detection: Users receive instant warnings when accessing suspicious URLs, helping prevent potential security breaches.
To use the Phishing URL Classification System locally, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/phishing-url-classification.git
- Navigate to the project directory:
cd phishing-url-classification/backend - Install the required Python libraries:
pip install -r requirements.txt
- Launch the Django server:
python manage.py runserver
- Access the system through a terminal:
http://localhost:8000/
- Python
- Django
- Pandas
- Scikit-learn
- React JS
Contributions are welcome! If you have suggestions, bug reports, or feature requests, please open an issue or submit a pull request..