Smart Contract Security Detection Based on Graph Neural Network

Ziteng Su

doi:10.47297/taposatWSP2633-456948.20250608

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Smart Contract Security Detection Based on Graph Neural Network

6卷第8期

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Smart Contract Security Detection Based on Graph Neural Network
Smart Contract Security Detection Based on Graph Neural Network
Tianjin University of Science and Technology, Tianjin, 300000, China
2025年6卷第8期页码：254-258
纸质出版：2025-08-30
DOI：10.47297/taposatWSP2633-456948.20250608
Accepted：

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(2025). Smart Contract Security Detection Based on Graph Neural Network. 科技理论与实践（英文版）, 6(8), 254-258.

Su Ziteng.Smart Contract Security Detection Based on Graph Neural Network[J].Theory and Practice of Science and Technology,2025,06(08):254-258.
(2025). Smart Contract Security Detection Based on Graph Neural Network. 科技理论与实践（英文版）, 6(8), 254-258. DOI： 10.47297/taposatWSP2633-456948.20250608.

Su Ziteng.Smart Contract Security Detection Based on Graph Neural Network[J].Theory and Practice of Science and Technology,2025,06(08):254-258. DOI： 10.47297/taposatWSP2633-456948.20250608.

摘要

Abstract

As the core component of blockchain applications

smart contracts are increasingly scrutinized for their security. Among various vulnerabilities

infinite loop flaws pose significant threats due to their hidden nature and potential for exhausting system resources. This paper proposes a static detection method based on Graph Convolutional Networks (GCNs)

which transforms smart contracts into control flow and data flow graphs. Through graph-based modeling and vectorized encoding

semantic features such as loop structures and function dependencies are effectively captured. An improved GCN architecture is employed to identify potential infinite execution patterns through neighborhood aggregation and graph-level representation learning. Experimental results demonstrate that the proposed method achieves high accuracy and F1-score across real-world contract datasets

offering an effective and scalable solution for smart contract vulnerability analysis.

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Keywords

references

Kolvart M, Poola M, Rull A. Smart contracts[J]. The Future of Law and etechnologies, 2016: 133-147.

Khan S N, Loukil F, Ghedira-Guegan C, et al. Blockchain smart contracts: Applications, challenges, and future trends[J]. Peer-to-peer Networking and Applications, 2021, 14: 2901-2925.

Chess B, McGraw G. Static analysis for security[J]. IEEE security & privacy, 2004, 2(6): 76-79.

Ayewah N, Pugh W, Hovemeyer D, et al. Using static analysis to find bugs[J]. IEEE software, 2008, 25(5): 22-29.

Subramanian V. Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch[M]. Packt Publishing Ltd, 2018.

Ghorbani M, Baghshah M S, Rabiee H R. MGCN: semi-supervised classification in multi-layer graphs with graph convolutional networks[C]//Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2019: 208-211.

Van Rossum G, Drake F L. An introduction to Python[M]. Bristol: Network Theory Ltd., 2003.

Parmar N, Vaswani A, Uszkoreit J, et al. Image transformer[C]//International conference on machine learning. PMLR, 2018: 4055-4064.