Dual-view Aware Smart Contract Vulnerability Detection for Ethereum
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916307495026688 |
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| author | Yao, Jiacheng Wang, Maolin Chen, Wanqi Jin, Chengxiang Zhou, Jiajun Yu, Shanqing Xuan, Qi |
| author_facet | Yao, Jiacheng Wang, Maolin Chen, Wanqi Jin, Chengxiang Zhou, Jiajun Yu, Shanqing Xuan, Qi |
| contents | The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widespread use. However, the non-tamperability of smart contracts, coupled with vulnerabilities caused by natural flaws or human errors, has brought unprecedented challenges to blockchain security. Therefore, in order to ensure the healthy development of blockchain technology and the stability of the blockchain community, it is particularly important to study the vulnerability detection techniques for smart contracts. In this paper, we propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet. The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences, capturing potential risk features from these two perspectives and integrating them for analysis, ultimately achieving effective contract vulnerability detection. Comprehensive experiments on the Ethereum dataset show that our method outperforms others in detecting vulnerabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dual-view Aware Smart Contract Vulnerability Detection for Ethereum Yao, Jiacheng Wang, Maolin Chen, Wanqi Jin, Chengxiang Zhou, Jiajun Yu, Shanqing Xuan, Qi Cryptography and Security Machine Learning The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widespread use. However, the non-tamperability of smart contracts, coupled with vulnerabilities caused by natural flaws or human errors, has brought unprecedented challenges to blockchain security. Therefore, in order to ensure the healthy development of blockchain technology and the stability of the blockchain community, it is particularly important to study the vulnerability detection techniques for smart contracts. In this paper, we propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet. The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences, capturing potential risk features from these two perspectives and integrating them for analysis, ultimately achieving effective contract vulnerability detection. Comprehensive experiments on the Ethereum dataset show that our method outperforms others in detecting vulnerabilities. |
| title | Dual-view Aware Smart Contract Vulnerability Detection for Ethereum |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2407.00336 |