ML Study of MaliciousTransactions in Ethereum
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917751433461760 |
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| author | Katz, Natan |
| author_facet | Katz, Natan |
| contents | Smart contracts are a major tool in Ethereum transactions. Therefore hackers can exploit them by adding code vulnerabilities to their sources and using these vulnerabilities for performing malicious transactions. This paper presents two successful approaches for detecting malicious contracts: one uses opcode and relies on GPT2 and the other uses the Solidity source and a LORA fine-tuned CodeLlama. Finally, we present an XGBOOST model that combines gas properties and Hexa-decimal signatures for detecting malicious transactions. This approach relies on early assumptions that maliciousness is manifested by the uncommon usage of the contracts' functions and the effort to pursue the transaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ML Study of MaliciousTransactions in Ethereum Katz, Natan Cryptography and Security Machine Learning Smart contracts are a major tool in Ethereum transactions. Therefore hackers can exploit them by adding code vulnerabilities to their sources and using these vulnerabilities for performing malicious transactions. This paper presents two successful approaches for detecting malicious contracts: one uses opcode and relies on GPT2 and the other uses the Solidity source and a LORA fine-tuned CodeLlama. Finally, we present an XGBOOST model that combines gas properties and Hexa-decimal signatures for detecting malicious transactions. This approach relies on early assumptions that maliciousness is manifested by the uncommon usage of the contracts' functions and the effort to pursue the transaction. |
| title | ML Study of MaliciousTransactions in Ethereum |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2408.08749 |