mlsanityData to 14 Oct 2026

ML Study of MaliciousTransactions in Ethereum

arXiv 2408.08749 ↗First posted 16 Aug 2024cs.CR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 27 references that are in the map. First posted on arXiv 16 Aug 2024.

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References · 4

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  4. 2022Parametric PDF for Goodness of Fitcited by 1

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