SCVHunter: Smart Contract Vulnerability Detection Based on Heterogeneous Graph Attention Network

Luo, Feng; Luo, Ruijie; Chen, Ting; Qiao, Ao; He, Zheyuan; Song, Shuwei; Jiang, Yu; Li, Sixing

doi:10.1145/3597503.3639213

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2024

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Cited by 3 publications

References 51 publications

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A Vulnerability Detection Method for Smart Contract Using Opcode Sequences with Variable Length

Liu,

Wang,

Chen

et al. 2024

Lecture Notes in Computer Science

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A Vulnerability Detection Method for Smart Contract Using Opcode Sequences with Variable Length

Liu,

Wang,

Chen

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

DoubleUp Roll: Double-spending in Arbitrum by Rolling It Back

Sun,

Li,

Peng

et al. 2024

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security

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A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training Techniques

Zhang,

Lu,

2024

Electronics

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The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities.

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SCVHunter: Smart Contract Vulnerability Detection Based on Heterogeneous Graph Attention Network

Cited by 3 publications

References 51 publications

A Vulnerability Detection Method for Smart Contract Using Opcode Sequences with Variable Length

A Vulnerability Detection Method for Smart Contract Using Opcode Sequences with Variable Length

DoubleUp Roll: Double-spending in Arbitrum by Rolling It Back

A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training Techniques

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