ML Study of MaliciousTransactions in Ethereum

• URL: http://arxiv.org/abs/2408.08749v1
• Date: Fri, 16 Aug 2024 13:50:04 GMT
• Title: ML Study of MaliciousTransactions in Ethereum
• Authors: Natan Katz,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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