SSRF_Framework

Overview

The SSRF_Framework is a tool designed to determine whether a given URL is benign or malicious, with a focus on identifying various malicious URLs and Server-Side Request Forgery (SSRF) vulnerabilities. The project provides two trained models, one utilizing LSTM and the other using XGBoost, to enhance the accuracy of the analysis.

Getting Started

To get started with the SSRF_Framework, follow these steps:

1. Clone the repository:

   git clone https://github.com/lolidrk/SSRF_Framework.git

2. Install the required libraries:

   Make sure you have the necessary Python libraries installed. You can install them using: --will not work (currently in progress; please manually install libraries for now)

   pip install -r requirements.txt

Running the Project

The SSRF_Framework by default uses the XGBoost model. To run the project with XGBoost, execute:

python3 accept.py

If you want to run the LSTM model, follow these steps:

1. Uncomment line number 4 in accept.py:

#import predicty_lstm

2. Change line number 18 from:

result = predict.make_prediction(url_features_data)

to:

result = predicty_lstm.make_prediction(url_features_data)

3. Run the project :

python3 accept.py

Make sure you have the correct libraries installed for the chosen model.

Contributing

Feel free to contribute to the SSRF_Framework by opening issues or creating pull requests. Your feedback and enhancements are highly appreciated.

License

This project is licensed under the MIT License.