Phishing Classifier 🌟

Problem Statement:

Anti-phishing refers to efforts to block phishing attacks. Phishing is a kind of cybercrime where attackers pose as known or trusted entities and contact individuals through email, text or telephone and ask them to share sensitive information. Typically, in a phishing email attack, and the message will suggest that there is a problem with an invoice, that there has been suspicious activity on an account, or that the user must login to verify an account or password. Users may also be prompted to enter credit card information or bank account details as well as other sensitive data. Once this information is collected, attackers may use it to access accounts, steal data and identities, and download malware onto the user’s computer.

Data Dictionary 📄✏ :

This dataset contains 48 features extracted from 5000 phishing webpages and 5000 legitimate webpages, which were downloaded from January to May 2015 and from May to June 2017. An improved feature extraction technique is employed by leveraging the browser automation framework (i.e., Selenium WebDriver), which is more precise and robust compared to the parsing approach based on regular expressions.

Anti-phishing researchers and experts may find this dataset useful for phishing features analysis, conducting rapid proof of concept experiments or benchmarking phishing classification models.

Requirements :

Ensure you have the following dependencies installed:

• Python (version 3.9)
• Jupyter Notebook
• Other dependencies (refer to the requirements.txt)

You can install the required Python packages using:

pip install -r requirements.txt

Setup 💿:

• Clone the repository:

git clone https://github.com/SINGHxTUSHAR/Phishing_Classifier.git
cd Phishing_Classifier

• Create a virtual environment (optional but recommended):

python -m venv venv

• Activate the virtual environment:
  • On Windows:

  venv\Scripts\activate

  • On macOS/Linux:

  source venv/bin/activate

Usage :

• Open the Jupyter Notebook:

jupyter notebook

• Navigate to the Phishing_Classifier.ipynb notebook and open it.
• Follow the instructions in the notebook to run the code cells.

Contributing :

If you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!

Suggestion:

If you have any suggestions for me related to this project, feel free to contact me at [email protected] or LinkedIn.

License :

This project is licensed under the MIT License - see the LICENSE file for details.