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Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer.

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  • 1School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China.

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Summary
This summary is machine-generated.

This study introduces MTCformer, a novel method for detecting Ponzi scheme contracts on blockchains. MTCformer effectively identifies these fraudulent smart contracts by analyzing source code structure and semantics, outperforming existing techniques.

Keywords:
Ponzi schemesblockchaindeep learningsmart contractsstructured sequences

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Area of Science:

  • Blockchain Technology
  • Cybersecurity
  • Machine Learning

Background:

  • Ponzi schemes increasingly exploit smart contracts, causing significant financial losses and damaging blockchain ecosystems.
  • Current detection methods rely on hand-crafted features, failing to capture the full structural and semantic nuances of smart contract source code.

Purpose of the Study:

  • To propose an advanced method for detecting Ponzi scheme smart contracts.
  • To overcome the limitations of traditional feature extraction by incorporating structural and semantic analysis.

Main Methods:

  • Developed MTCformer (Multi-channel Text Convolutional Neural Networks and Transformer).
  • Utilized Structure-Based Traversal (SBT) to convert Abstract Syntax Trees (AST) into specialized code token sequences.
  • Employed multi-channel TextCNN for local feature learning and Transformer for long-range dependency capture.
  • Incorporated a cost-sensitive loss function for classification.

Main Results:

  • MTCformer demonstrated superior performance in detecting Ponzi scheme contracts.
  • The method effectively captured both local and global features within smart contract source code.
  • Experimental results confirmed MTCformer's advantage over state-of-the-art approaches.

Conclusions:

  • MTCformer offers a robust and effective solution for identifying Ponzi scheme contracts.
  • The proposed approach enhances blockchain security by improving the detection of malicious smart contracts.