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All Projects → m-zakeri → iust_deep_fuzz

m-zakeri / iust_deep_fuzz

Licence: MIT license
Advanced file format fuzzer based-on deep neural language models.

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deep-learningtensorflowkerasrecurrent-neural-networksfuzz-testingtest-datacode-coveragevulnerability-detectionneural-language-model

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IUST-DeepFuzz

Before getting started, please read the documentation:

IUST-DeepFuzz Website and Documentation

and watch the DeepFuzz demo:

Video demo

Getting Started

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

Install

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

Running

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

Available Pre-trained Models

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 models for PDF file format. Our best trained model is available at model_checkpoint/best_models

Available Fuzzing Scripts

ISUT-DeepFuzz has implemented four new deep models and two new fuzz algorithms: DataNeuralFuzz and MetadataNeuralFuzz, as our contributions of the 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 the SampleFuzz algorithm introduced in https://arxiv.org/abs/1701.07232.

Available Dataset

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

Read More

Recently, I wrote a blog post about our DeepFuzz paper:

FAQs

if you have any questions, please do not hesitate to contact me:

[email protected]

Last update: September 12, 2022

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