Smart Contract Vulnerability Detection using Graph Neural Network

By Yuan Zhuang, Zhenguang Liu, Peng Qian, Qi Liu, Xiang Wang, Qinming He. International Joint Conference on Artificial Intelligence, 2020

440 citations recorded by the source corpus.

The security problems of smart contracts have drawn extensive attention due to the enormous financial losses caused by vulnerabilities. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low detection accuracy. In this paper, we explore using graph neural networks (GNNs) for smart contract vulnerability detection. Particularly, we construct a contract graph to represent both syntactic and semantic structures of a smart contract function. To highlight the major nodes, we design an elimination phase to normalize the graph. Then, we propose a degree-free graph convolutional neural network (DR-GCN) and a novel temporal message propagation network (TMP) to learn from the normalized graphs for vulnerability detection. Extensive experiments show that our proposed approach significantly outperforms state-of-the-art methods in detecting three different types of vulnerabilities.

Subfields: Control and Systems Engineering, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/81861b6615015a8af45b4119a1144d9dd5bef4a7

How researchers see this paper

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In Information Systems

1. Xiang Wang
2. Xiangnan He
3. Liqiang Nie
4. Tat-seng Chua
5. Yin-wei Wei
6. Zhenguang Liu
7. Qi Li
8. Xuanping Li

Citations in the corpus

• Contrastive Learning for Cold-Start Recommendation (2021, background): “• LightGCN [14] Based on graph convolutional networks [30, 55], it learns the high-order CF signal and injects it into the collaborative embedding to optimize the recommendation.”
• Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection (2021, methodology): “[20] proposes to convert the source code of a contract into the contract graph and constructs graph neural networks as the detection model.”