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Enhancing fraud detection in the Ethereum blockchain using ensemble learning.

Zhexian Gu1,2,3, Omar Dib1,2,3

  • 1Department of Computer Science, Kean University, Union, New Jersey, United States.

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|March 10, 2025
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Summary
This summary is machine-generated.

This study introduces an ensemble learning method to detect fraudulent Ethereum blockchain transactions, enhancing security for decentralized finance and online commerce. The system achieves over 98% accuracy, aiding miners and authorities in combating illicit activities.

Keywords:
BlockchainCryptocurrencyCybersecurityEnsemble learningEthereumFinancial securityFraud detectionMachine learning

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Area of Science:

  • Blockchain Technology
  • Cybersecurity
  • Machine Learning

Background:

  • The Ethereum blockchain facilitates decentralized transactions but faces increasing security vulnerabilities due to rising online fraud, including money laundering and phishing.
  • The exponential growth of e-commerce amplifies the risk of fraudulent activities on decentralized platforms.
  • Existing security measures struggle to keep pace with sophisticated fraudulent schemes on the Ethereum network.

Purpose of the Study:

  • To develop and evaluate an ensemble learning approach for accurate detection of fraudulent transactions on the Ethereum blockchain.
  • To integrate a decision-making tool into the Ethereum validation process for real-time identification of illicit activities.
  • To provide a system that assists both blockchain miners and governmental organizations in monitoring and combating blockchain fraud.

Main Methods:

  • Employed data pre-processing techniques and evaluated multiple machine learning algorithms: logistic regression, Isolation Forest, support vector machine, Random Forest, XGBoost, and recurrent neural network.
  • Utilized grid search for hyperparameter tuning to optimize individual model performance.
  • Developed an ensemble model combining Random Forest, XGBoost, and support vector machine for enhanced classification accuracy.

Main Results:

  • The proposed ensemble learning approach achieved high performance across key metrics, exceeding 98% in accuracy, precision, recall, and F1-score.
  • Individual models were fine-tuned, and an ensemble strategy further improved the overall classification performance.
  • The system demonstrated practical applicability with a fast inference time of 0.13 seconds.

Conclusions:

  • The ensemble learning framework effectively detects fraudulent Ethereum blockchain transactions with high accuracy and efficiency.
  • The developed system offers a robust solution for enhancing the security of decentralized platforms and combating financial crime.
  • The approach is suitable for real-world deployment, providing valuable support for network security and regulatory oversight.