Before getting started please read the documentation:

IUST-DeepFuzz Website and Documentation

In the current release (0.3.0) you can use IUST-DeepFuzz for test data generation and then fuzzing every application.

You need to have Python 3.6.x and and up-to-date TensorFlow and Keras frameworks on your computer.

• Install Python 3.6.x
• Install TensorFlow
• Install Keras
• Clone the IUST-DeepFuzz repository: git clone https://github.com/m-zakeri/iust_deep_fuzz.git or download the latest version https://github.com/m-zakeri/iust_deep_fuzz.git
• IUST-DeepFuzz is almost ready for test data generation!

• Configure the config.py work with your dataset and to set other paths settings.
• Find the script of specific algorithm that you need.
• Run the script in command line: python script_name.py
• Wait until your file format learn and your test data is generate!

A pre-trained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. For the time being, we provided some pre-trained model for PDF file format. Our best trained model is available at model_checkpoint/best_models

ISUT-DeepFuzz has implemented four new deep models and two new fuzz algorithms: DataNeuralFuzz and MetadataNeuralFuzz as our contribution in mentioned thesis. The following algorithms to generate and fuzz test data are available in the current release (r0.3.0):

• data_neural_fuzz.py: To implement the DataNeuralFuzz algorithm for fuzzing data in the files.
• metadata_neural_fuzz.py: To implement MetadataNeuralFuzz for fuzzing metadata in the files.
• learn_and_fuzz_3_sample_fuzz.py: To implement SampleFuzz algorithm introduced in https://arxiv.org/abs/1701.07232.

Various file format for learning with IUST-DeepFuzz and then fuzz testing is available at dataset directory.

IUST-DeepFuzz Website and Documentation

Last update: April 13, 2020