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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.12370 |
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| _version_ | 1866916656464265216 |
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| author | Jin, Yihong Yang, Ze Xu, Xinhe |
| author_facet | Jin, Yihong Yang, Ze Xu, Xinhe |
| contents | As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain Jin, Yihong Yang, Ze Xu, Xinhe Machine Learning Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing Social and Information Networks I.2.1 As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem. |
| title | Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing Social and Information Networks I.2.1 |
| url | https://arxiv.org/abs/2412.12370 |