In this study, we empirically study different existing Deep Learning Based Vulnerability Detection techniques for real world vulnerabilities. We test the feasibility of existing techniques in two different datasets.

1. Part of Devign dataset (often referred to as FFMpeg Qemu dataset in the project).
2. Our Collected vulnerabilities from Chrome and Debian issue trackers (Often referred as Chrome Debian or Verum dataset in this project).

To download data

cd data;
bash get_data.sh

To download (some of) pretrained models

cd models;
bash get_models.sh

Some of the tools in this study can be used for a new datasets. In order for doing that, we use Joern for parsing the C code in this repository.

cd code-slicer/joern;
bash build.sh

Once the build is successful, go to the folder you want to perform your experiment, create a folder named raw_code and create every functions in separate C files. We followed the custom to file names <name>_<VUL>.c, wehre the <VUL> is the Vulnerability identifier of the function (0 for benign, 1 for vulnerable).

1. You have to extract the slices from the parsed code. Modify the data_processing/extract_slices.ipynb for extracting slice. This will generate a file <data_name>_full_data_with_slices.json in your data directory.

2. Run data_processing/create_ggnn_data.py for formatting data into different formats.

3. Update data_processing/full_data_prep_script.ipynb to input to the GGNN.

1. Clone our implemetation of Devign from here.
2. Use the following parameters "node_features" as "--node_tag", "graph" as --graph_tag, and targets as --label_tag.
3. User --save_after_ggnn flag for saving the data after processing through GGNN.

The running APIs are exposed by this file. Moddify the parameters to fit your need.

To try ReVeal on Chrome Debian(Verum) dataset,

cd Vuld_SySe/representation_learning;
bash run_verum.sh

To try ReVeal on Devign dataset,

cd Vuld_SySe/representation_learning;
bash run_devign.sh

We include different scripts for running other models (i.e. VulDeePecker, SySeVR, Draper) under scripts/ and real_data_scripts/ folders.

We are using several different components from the state-of-the-art research. Please cite accordingly to pay due attributes and credits to the authors.

1. If you use Code-Slicer portion from this repository, please cite the following

@inproceedings{yamaguchi2014modeling,
  title={Modeling and discovering vulnerabilities with code property graphs},
  author={Yamaguchi, Fabian and Golde, Nico and Arp, Daniel and Rieck, Konrad},
  booktitle={2014 IEEE Symposium on Security and Privacy},
  pages={590--604},
  year={2014},
  organization={IEEE}
}

2. If you use Devign, please cite,

@inproceedings{zhou2019devign,
  title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
  author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
  booktitle={Advances in Neural Information Processing Systems},
  pages={10197--10207},
  year={2019}
}

3. If you refer to empirical finding reported in the paper, please cite our pre-print as

@article{chakraborty2020deep,
  title={Deep Learning based Vulnerability Detection: Are We There Yet?},
  author={Chakraborty, Saikat and Krishna, Rahul and Ding, Yangruibo and Ray, Baishakhi},
  journal={arXiv preprint arXiv:2009.07235},
  year={2020}
}