Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910532540301312 |
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| author | Huynh, Phuong Duy Dau, Son Hoang Li, Xiaodong Luong, Phuc Viterbo, Emanuele |
| author_facet | Huynh, Phuong Duy Dau, Son Hoang Li, Xiaodong Luong, Phuc Viterbo, Emanuele |
| contents | The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum blockchain, causing considerable financial losses to many crypto investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code. This contract-code-based approach, while achieving very high accuracy, is not robust because a Ponzi developer can fool a detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected. On the contrary, a transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. In this paper, we aim to improve the accuracy of the transaction-based models by employing time-series features, which turn out to be crucial in capturing the life-time behaviour a Ponzi application but were completely overlooked in previous works. We propose a new set of 85 features (22 known account-based and 63 new time-series features), which allows off-the-shelf machine learning algorithms to achieve up to 30% higher F1-scores compared to existing works. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_16391 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum Huynh, Phuong Duy Dau, Son Hoang Li, Xiaodong Luong, Phuc Viterbo, Emanuele Cryptography and Security Computational Engineering, Finance, and Science Machine Learning Statistical Finance The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum blockchain, causing considerable financial losses to many crypto investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code. This contract-code-based approach, while achieving very high accuracy, is not robust because a Ponzi developer can fool a detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected. On the contrary, a transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. In this paper, we aim to improve the accuracy of the transaction-based models by employing time-series features, which turn out to be crucial in capturing the life-time behaviour a Ponzi application but were completely overlooked in previous works. We propose a new set of 85 features (22 known account-based and 63 new time-series features), which allows off-the-shelf machine learning algorithms to achieve up to 30% higher F1-scores compared to existing works. |
| title | Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum |
| topic | Cryptography and Security Computational Engineering, Finance, and Science Machine Learning Statistical Finance |
| url | https://arxiv.org/abs/2308.16391 |