EthCluster: An Unsupervised Static Analysis Method for Ethereum Smart Contract
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912325693341696 |
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| author | Huang, Hong-Sheng Ho, Jen-Yi Chen, Hao-Wen Sun, Hung-Min |
| author_facet | Huang, Hong-Sheng Ho, Jen-Yi Chen, Hao-Wen Sun, Hung-Min |
| contents | Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify vulnerabilities in the Solidity source code of Ethereum smart contracts. To address the challenges associated with real-world smart contracts, our training data is derived from actual vulnerability samples obtained from datasets such as SmartBugs Curated and the SolidiFI Benchmark. These datasets enable us to develop a robust unsupervised static analysis method for detecting five specific vulnerabilities: Reentrancy, Access Control, Timestamp Dependency, tx.origin, and Unchecked Low-Level Calls. We employ clustering algorithms to identify outliers, which are subsequently classified as vulnerable smart contracts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EthCluster: An Unsupervised Static Analysis Method for Ethereum Smart Contract Huang, Hong-Sheng Ho, Jen-Yi Chen, Hao-Wen Sun, Hung-Min Cryptography and Security Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify vulnerabilities in the Solidity source code of Ethereum smart contracts. To address the challenges associated with real-world smart contracts, our training data is derived from actual vulnerability samples obtained from datasets such as SmartBugs Curated and the SolidiFI Benchmark. These datasets enable us to develop a robust unsupervised static analysis method for detecting five specific vulnerabilities: Reentrancy, Access Control, Timestamp Dependency, tx.origin, and Unchecked Low-Level Calls. We employ clustering algorithms to identify outliers, which are subsequently classified as vulnerable smart contracts. |
| title | EthCluster: An Unsupervised Static Analysis Method for Ethereum Smart Contract |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2504.09977 |