AMEVulDetector

This repo is a python implementation of Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion.

Overview

The overall architecture of our proposed method is illustrated in the Figure, which consists of three components: (a) a local expert pattern extraction tool, which extracts vulnerability-specific expert patterns from the code; (b) a graph construction and normalization module, which transforms the source code into a global semantic graph; and (c) an attentive multi-encoder network that combines expert patterns and the graph feature for vulnerability detection and outputs explainable weights.

Citation

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

@inproceedings{liu2021smart,
  title={Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion},
  author={Liu, Zhenguang and Qian, Peng and Wang, Xiang and Zhu, Lei and He, Qinming and Ji, Shouling},
   booktitle={IJCAI},
  pages={2751--2759},
  year={2021}
}

Required Packages

python 3 or above
TensorFlow 2.0 or above
sklearn 0.20.2
numpy 1.18 or above

Run the following script to install the required packages.

pip install --upgrade pip
pip install tensorflow==2.0
pip install scikit-learn==0.20.2
pip install numpy==1.18

Dataset

We empirically evaluate our proposed method on two benchmark datasets, namely Ethereum smart contract (ESC) and VNT Chain smart contract (VSC). Following Zhuang et al., 2020, we conduct experiments for reentrancy and timestamp dependence vulnerability on ESC, and evaluate infinite loop vulnerability on VSC.

The specific description of the two datasets can be found in Zhuang et al., 2020, while the source code are released on Github.

More details for the dataset instruction can be found on our dataset page at Smart-Contract-Dataset, which is continuously improving.

Getting started

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

Examples:

python3 AMEVulDetector.py
python3 AMEVulDetector.py --model EncoderWeight --lr 0.002 --dropout 0.2 --epochs 100 --batch_size 32

Case Study

We further present a case study in the following Figure, where the withdraw function is a real-world smart contract function that is vulnerable to reentrancy vulnerability. Particularly, we characterize the function code as both the global graph and three local patterns for reentrancy (respectively corresponding to the enoughBalance, callValueInvoc, balanceDeduction), which are shown in the left of the following figure. Intuitively, The weights of the global graph feature and each local pattern feature are illustrated in the right of the following figure, where our system is able to clearly explain the reasons behind our prediction.

Note

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

Reference

Zhuang, Yuan and Liu, Zhenguang and Qian, Peng, et al. Smart Contract Vulnerability Detection using Graph Neural Network. IJCAI, 2020. GNNSCVulDetector
Qian P, Liu Z, He Q, et al. Towards automated reentrancy detection for smart contracts based on sequential models. 2020. ReChecker

Messi-Q/AMEVulDetector