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19 datasets found
  1. Ethereum Fraud Detection Dataset

    Updated Jan 3, 2021
  2. A

    ‘Ethereum Fraud Detection Dataset’ analyzed by Analyst-2

    Updated Aug 4, 2020
  3. Ethereum Fraud Dataset

    Updated Sep 14, 2022
  4. Ethereum_Fraud_Detection

    Updated Mar 2, 2021
  5. D

    Data from: Detection of illicit accounts over the Ethereum blockchain

    csv, txt
    Updated Feb 19, 2021
  6. Ethereum Phishing Scams dataset - Eth-PSD

    Updated Nov 6, 2022
  7. A

    ‘Ethereum Price Trend - Historical data’ analyzed by Analyst-2

    Updated Sep 30, 2021
  8. f

    The comparison between (smart contract)-based delegate contract signing and...

    Updated Aug 19, 2022
  9. Ethereum Block data

    Updated Jun 25, 2021
  10. Ethereum Historical Dataset

    Updated Apr 16, 2020
  11. Value of cryptocurrency theft globally 2016-2020

    Updated Jan 10, 2023
  12. Dataset-Fraud-ETH-Cleaned

    Updated Apr 19, 2021
  13. Monthly size of crypto theft 2020-2022

    Updated Feb 3, 2022
  14. t

    ETHBGN Ethereum Bulgarian Lev - Currency Exchange Rate Live Price Chart

    Updated Dec 18, 2020
  15. Ethereum Historical Data

    Updated Oct 6, 2018
  16. Litecoin LTC/USD price history up until Jan 31, 2023

  17. Worldwide blockchain market value share 2020, by sector

    Updated May 23, 2022
  18. Ethereum Data

    Updated Mar 25, 2022
  19. Ethereum Price Trend - Historical data

    Updated May 13, 2021
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DataverseNL (2021). Detection of illicit accounts over the Ethereum blockchain [Dataset].

Data from: Detection of illicit accounts over the Ethereum blockchain

Related Article
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csv(1016388), txt(506)Available download formats
Dataset updated
Feb 19, 2021
Dataset provided by

CC0 1.0 Universal Public Domain Dedication
License information was derived automatically


The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works.

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