GPSCVulDetector

This repo is a python implementation of combining graph neural network with expert knowledge for smart contract vulnerability detection.

Citation

Please use this citation in your paper if you refer to our paper or code.

@article{liu2023combining,
  title={Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection},
  author={Liu, Zhenguang and Qian, Peng and Wang, Xiaoyang and Zhuang, Yuan and Qiu, Lin and Wang, Xun},
  journal={IEEE Transactions on Knowledge \& Data Engineering},
  volume={35},
  number={02},
  pages={1296--1310},
  year={2023},
  publisher={IEEE Computer Society}
}

Requirements

Required Packages

python 3+
TensorFlow 2.0
numpy 1.18.2
sklearn 0.20.2

Run the following script to install the required packages.

pip install --upgrade pip
pip install tensorflow==2.0
pip install numpy==1.18.2
pip install scikit-learn==0.20.2

Graph extractor & Pattern extractor

Graph: The contract graph and its feature are extracted by the automatic graph extractor in the graph_extractor_example directory (or refer to our previous methods).
Pattern: The expert pattern and its feature are extracted by the automatic pattern extractor in the pattern_extractor_example directory.

Notably, you can also use the features extracted in AMEVulDetector.

If any question, please email to messi.qp711@gmail.com.

Running Project

To run program, please use this command: python3 GPSCVulDetector.py.
Also, you can set specific hyperparameters, and all the hyperparameters can be found in parser.py.

Examples:

python3 GPSCVulDetector.py
python3 GPSCVulDetector.py --model CGE --lr 0.002 --dropout 0.2 --epochs 100 --batch_size 32

References

Smart Contract Vulnerability Detection Using Graph Neural Networks. IJCAI 2020. GNNSCVulDetector.

@inproceedings{ijcai2020-454,
  title     = {Smart Contract Vulnerability Detection using Graph Neural Network},
  author    = {Zhuang, Yuan and Liu, Zhenguang and Qian, Peng and Liu, Qi and Wang, Xiang and He, Qinming},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization}, 
  pages     = {3283--3290},
  year      = {2020},
}

Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models. IEEE Access. ReChecker.

@article{qian2020towards,
  title={Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models},
  author={Qian, Peng and Liu, Zhenguang and He, Qinming and Zimmermann, Roger and Wang, Xun},
  journal={IEEE Access},
  year={2020},
  publisher={IEEE}
}