iust_deep_fuzz
Advanced file format fuzzer based-on deep neural language models.
Science Score: 36.0%
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○Scientific vocabulary similarity
Low similarity (14.6%) to scientific vocabulary
Keywords
Repository
Advanced file format fuzzer based-on deep neural language models.
Basic Info
- Host: GitHub
- Owner: m-zakeri
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://m-zakeri.github.io/iust_deep_fuzz/
- Size: 229 MB
Statistics
- Stars: 41
- Watchers: 5
- Forks: 16
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
IUST-DeepFuzz
Before getting started, please read the documentation:
IUST-DeepFuzz Website and Documentation
and watch the DeepFuzz 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.
* 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/iustdeepfuzz.git
* IUST-DeepFuzz is almost ready for test data generation!
Running
- Configure the
config.pywork 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 modelcheckpoint/bestmodels
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:
Last update: September 12, 2022
Owner
- Name: Morteza Zakeri
- Login: m-zakeri
- Kind: user
- Location: Planet Earth
- Company: @Micropedia
- Website: m-zakeri.github.io
- Twitter: _zakeri_
- Repositories: 8
- Profile: https://github.com/m-zakeri
Ph.D. candidate, software engineer, machine intelligence