Smart contract vulnerability detection method based on pre-training and novel timing graph neural network
To address the limitations of current deep learning-based methods in extracting contract bytecode features and representing vulnerability semantics, as well as the shortcomings of the traditional graph neural networks in learning temporal information from contract statements, a method for detecting...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024163/ |
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| _version_ | 1841539211963924480 |
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| author | ZHUANG Yuan FAN Zekai WANG Cheng SUN Jianguo LI Yaolin |
| author_facet | ZHUANG Yuan FAN Zekai WANG Cheng SUN Jianguo LI Yaolin |
| author_sort | ZHUANG Yuan |
| collection | DOAJ |
| description | To address the limitations of current deep learning-based methods in extracting contract bytecode features and representing vulnerability semantics, as well as the shortcomings of the traditional graph neural networks in learning temporal information from contract statements, a method for detecting vulnerabilities in contracts was proposed based on pre-trained and temporal graph neural network. Firstly, the pre-trained model was used to transform smart contract bytecode into a vulnerability semantics-aware contract graph structure. Then, combined with a self-attention mechanism, the event-driven temporal graph neural network was designed to extract temporal information during contract execution. Finally, focusing on reentrant vulnerabilities, timestamp dependency vulnerabilities, and Tx.origin authentication vulnerabilities, extensive experiments were conducted on a dataset of 120 932 actual contracts. The results show that the proposed method significantly outperforms existing approaches. |
| format | Article |
| id | doaj-art-8986762f7d6e4792be7ea606089b5a1f |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8986762f7d6e4792be7ea606089b5a1f2025-01-14T07:25:04ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-09-014510111473359234Smart contract vulnerability detection method based on pre-training and novel timing graph neural networkZHUANG YuanFAN ZekaiWANG ChengSUN JianguoLI YaolinTo address the limitations of current deep learning-based methods in extracting contract bytecode features and representing vulnerability semantics, as well as the shortcomings of the traditional graph neural networks in learning temporal information from contract statements, a method for detecting vulnerabilities in contracts was proposed based on pre-trained and temporal graph neural network. Firstly, the pre-trained model was used to transform smart contract bytecode into a vulnerability semantics-aware contract graph structure. Then, combined with a self-attention mechanism, the event-driven temporal graph neural network was designed to extract temporal information during contract execution. Finally, focusing on reentrant vulnerabilities, timestamp dependency vulnerabilities, and Tx.origin authentication vulnerabilities, extensive experiments were conducted on a dataset of 120 932 actual contracts. The results show that the proposed method significantly outperforms existing approaches.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024163/blockchainsmart contractvulnerability detectionpre-training modelgraph neural network |
| spellingShingle | ZHUANG Yuan FAN Zekai WANG Cheng SUN Jianguo LI Yaolin Smart contract vulnerability detection method based on pre-training and novel timing graph neural network Tongxin xuebao blockchain smart contract vulnerability detection pre-training model graph neural network |
| title | Smart contract vulnerability detection method based on pre-training and novel timing graph neural network |
| title_full | Smart contract vulnerability detection method based on pre-training and novel timing graph neural network |
| title_fullStr | Smart contract vulnerability detection method based on pre-training and novel timing graph neural network |
| title_full_unstemmed | Smart contract vulnerability detection method based on pre-training and novel timing graph neural network |
| title_short | Smart contract vulnerability detection method based on pre-training and novel timing graph neural network |
| title_sort | smart contract vulnerability detection method based on pre training and novel timing graph neural network |
| topic | blockchain smart contract vulnerability detection pre-training model graph neural network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024163/ |
| work_keys_str_mv | AT zhuangyuan smartcontractvulnerabilitydetectionmethodbasedonpretrainingandnoveltiminggraphneuralnetwork AT fanzekai smartcontractvulnerabilitydetectionmethodbasedonpretrainingandnoveltiminggraphneuralnetwork AT wangcheng smartcontractvulnerabilitydetectionmethodbasedonpretrainingandnoveltiminggraphneuralnetwork AT sunjianguo smartcontractvulnerabilitydetectionmethodbasedonpretrainingandnoveltiminggraphneuralnetwork AT liyaolin smartcontractvulnerabilitydetectionmethodbasedonpretrainingandnoveltiminggraphneuralnetwork |