GraphDeeSmartContract

This repo is a python implementation of smart contract vulnerability detection using graph neural networks (DR-GCN).

Requirements

Required Packages

• python 3+ (python 3.7 used in our project)
• PyTorch 1.0.0
• numpy 1.18.2
• sklearn 0.20.2

Run the following script to install the required packages.

pip install --upgrade pip
pip install torch==1.0.0
pip install numpy==1.18.2
pip install scikit-learn==0.20.2

Citation

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

@inproceedings{zhuang2020smart,
  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={IJCAI},
  pages={3283--3290},
  year={2020}
}

Running project

• To run program, please use this command: python3 SMVulDetector.py.
• In addition, you can set specific hyper-parameters, and all the hyper-parameters can be found in parser.py.

Examples:

python3 SMVulDetector.py --dataset training_data/REENTRANCY_CORENODES_1671
python3 SMVulDetector.py --dataset training_data/REENTRANCY_CORENODES_1671 --model gcn_modify --n_hidden 192 --lr 0.001 -f 64,64,64 --dropout 0.1 --vector_dim 100 --epochs 50 --lr_decay_steps 10,20 

Using script： Repeating 10 times for different seeds with train.sh.

for i in $(seq 1 10);
do seed=$(( ( RANDOM % 10000 )  + 1 ));
python3 SMVulDetector.py --model gcn_modify --seed $seed | tee logs/smartcheck_"$i".log;
done

Then, you can find the training results in the logs/.

Dataset

For original dataset, please turn to the dataset repo.

The normalized train data can be found in training_data/REENTRANCY_CORENODES_1671, REENTRANCY_FULLNODES_1671

Note that the instruction of constructing the dataset can be found in the GraphLearning, and the XXX_node_attributes can be obtained using our designed tools.

Reference

1. A fraction of the code reuses the code of graph_unet.
2. Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017.