Enhancing Smart Contract Security Analysis with Execution Property Graphs
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913790000365568 |
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| author | Qin, Kaihua Ye, Zhe Wang, Zhun Li, Weilin Zhou, Liyi Zhang, Chao Song, Dawn Gervais, Arthur |
| author_facet | Qin, Kaihua Ye, Zhe Wang, Zhun Li, Weilin Zhou, Liyi Zhang, Chao Song, Dawn Gervais, Arthur |
| contents | Smart contract vulnerabilities have led to significant financial losses, with their increasing complexity rendering outright prevention of hacks increasingly challenging. This trend highlights the crucial need for advanced forensic analysis and real-time intrusion detection, where dynamic analysis plays a key role in dissecting smart contract executions. Therefore, there is a pressing need for a unified and generic representation of smart contract executions, complemented by an efficient methodology that enables the modeling and identification of a broad spectrum of emerging attacks.
We introduce Clue, a dynamic analysis framework specifically designed for the Ethereum virtual machine. Central to Clue is its ability to capture critical runtime information during contract executions, employing a novel graph-based representation, the Execution Property Graph. A key feature of Clue is its innovative graph traversal technique, which is adept at detecting complex attacks, including (read-only) reentrancy and price manipulation. Evaluation results reveal Clue's superior performance with high true positive rates and low false positive rates, outperforming state-of-the-art tools. Furthermore, Clue's efficiency positions it as a valuable tool for both forensic analysis and real-time intrusion detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_14046 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing Smart Contract Security Analysis with Execution Property Graphs Qin, Kaihua Ye, Zhe Wang, Zhun Li, Weilin Zhou, Liyi Zhang, Chao Song, Dawn Gervais, Arthur Cryptography and Security Smart contract vulnerabilities have led to significant financial losses, with their increasing complexity rendering outright prevention of hacks increasingly challenging. This trend highlights the crucial need for advanced forensic analysis and real-time intrusion detection, where dynamic analysis plays a key role in dissecting smart contract executions. Therefore, there is a pressing need for a unified and generic representation of smart contract executions, complemented by an efficient methodology that enables the modeling and identification of a broad spectrum of emerging attacks. We introduce Clue, a dynamic analysis framework specifically designed for the Ethereum virtual machine. Central to Clue is its ability to capture critical runtime information during contract executions, employing a novel graph-based representation, the Execution Property Graph. A key feature of Clue is its innovative graph traversal technique, which is adept at detecting complex attacks, including (read-only) reentrancy and price manipulation. Evaluation results reveal Clue's superior performance with high true positive rates and low false positive rates, outperforming state-of-the-art tools. Furthermore, Clue's efficiency positions it as a valuable tool for both forensic analysis and real-time intrusion detection. |
| title | Enhancing Smart Contract Security Analysis with Execution Property Graphs |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2305.14046 |