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Detection of Ponzi scheme on Ethereum using machine learning algorithms.

Ifeyinwa Jacinta Onu1, Abiodun Esther Omolara2, Moatsum Alawida3

  • 1Department of Computer Science, University of Abuja, Gwagwalada, Nigeria. sylviaajah57@gmail.com.

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This summary is machine-generated.

This study introduces a new machine learning approach to detect Ponzi schemes on the Ethereum blockchain. The random forest model achieved high accuracy, outperforming previous methods in identifying fraudulent financial activities.

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Area of Science:

  • Cybersecurity
  • Financial Technology (FinTech)
  • Machine Learning Applications

Background:

  • Ponzi schemes pose significant online security threats, particularly in regions with high poverty rates.
  • Existing methods for detecting Ponzi schemes, often relying on transaction data, suffer from accuracy limitations and data deficiencies.
  • The rapid growth of fraudulent online businesses necessitates advanced detection techniques.

Purpose of the Study:

  • To propose and evaluate a novel machine learning-based approach for detecting Ponzi schemes on the Ethereum network.
  • To compare the performance of Random Forest (RF), Neural Network (NN), and K-Nearest Neighbor (KNN) algorithms in identifying Ponzi schemes.
  • To enhance the efficiency of Ponzi scheme detection by reducing the number of relevant features.

Main Methods:

  • Utilized over 20,000 Ethereum transaction network datasets from Kaggle.
  • Preprocessed datasets for training and evaluating machine learning models: Random Forest (RF), Neural Network (NN), and K-Nearest Neighbor (KNN).
  • Feature selection was employed to reduce the dataset complexity from 70 to 10 features while maintaining accuracy.

Main Results:

  • The Random Forest (RF) model exhibited superior performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667.
  • Comparative analysis indicated that the proposed RF model achieves higher accuracy than previous detection methods.
  • The method successfully identified key fraud features, reducing dimensionality significantly without compromising detection capabilities.

Conclusions:

  • The developed machine learning approach, particularly using Random Forest, is effective in detecting Ponzi schemes on the Ethereum blockchain.
  • This method offers a robust solution for identifying sophisticated Ponzi schemes early in their lifecycle.
  • The findings provide valuable insights for combating financial fraud and enhancing online security.