This repository contains code and resources for a research project focused on phishing detection using URL analysis. The goal of this project is to leverage machine learning techniques to develop a highly accurate model that can classify URLs as either legitimate or phishing. By analyzing various features of URLs, such as domain reputation, URL length, and presence of suspicious keywords, the model aims to provide effective protection against phishing attacks. Through the use of advanced machine learning algorithms, this project aims to enhance the security of online users and mitigate the risks associated with phishing attempts.
Phishing attacks are a prevalent and persistent form of cybercrime, posing a significant threat to individuals, organizations, and even governments. These attacks involve malicious actors attempting to deceive unsuspecting users into revealing sensitive information, such as passwords, credit card details, or personal data. The consequences of falling victim to a phishing attack can be severe, ranging from financial loss to identity theft and reputational damage.
To combat this ever-evolving threat landscape, this research project focuses on leveraging advanced URL analysis techniques to identify and classify phishing URLs. By analyzing various features of URLs, such as domain reputation, URL length, presence of suspicious keywords, and other relevant indicators, the project aims to develop a highly accurate machine learning model. This model will be capable of distinguishing between legitimate URLs and phishing URLs, providing effective protection against phishing attacks.
The ultimate goal of this project is to enhance the security of online users and mitigate the risks associated with phishing attempts. By developing a robust and reliable phishing detection model, individuals, businesses, and organizations can proactively defend against phishing attacks, safeguarding their sensitive information and preserving their digital trust.
To use the code in this repository, follow these steps:
- Clone the repository:
git clone https://github.com/vyomaaverse/phishing-research.git - Install the required dependencies:
pip install -r requirements.txt
To train and evaluate the phishing detection model, follow these steps:
- Open the terminal and navigate to the project directory.
- Run the following command to train the model:
python hybrid_RF.py
Make sure you have the required dependencies installed before running the commands. For more details, refer to the Installation section.
Contributions are welcome! If you would like to contribute to this project, please follow these guidelines:
- Fork the repository
- Create a new branch:
git checkout -b my-feature - Make your changes and commit them:
git commit -m 'Add some feature' - Push to the branch:
git push origin my-feature - Submit a pull request
This project is licensed under the MIT License.