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15 datasets found
  1. A

    ‘Ethereum Fraud Detection Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Nov 15, 2021
  2. k

    Ethereum-Fraud-Detection-Dataset

    • kaggle.com
    Updated Apr 18, 2021
  3. Ethereum Fraud Detection Dataset

    • kaggle.com
    zip
    Updated Jan 3, 2021
  4. Ethereum_Fraud_Detection

    • kaggle.com
    zip
    Updated Mar 2, 2021
  5. D

    Data from: Detection of illicit accounts over the Ethereum blockchain

    • dataverse.nl
    • dataverse.harvard.edu
    csv, txt
    Updated Feb 19, 2021
  6. d

    Detection of illicit accounts over the Ethereum blockchain - Dataset -...

    • b2find.dkrz.de
    Updated Feb 18, 2020
  7. Ethereum Fraud Dataset

    • kaggle.com
    zip
    Updated Sep 14, 2022
  8. dataset for fraud detection

    • kaggle.com
    zip
    Updated May 18, 2023
  9. f

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

    • figshare.com
    xls
    Updated Jun 6, 2023
  10. Dataset-Fraud-ETH-Cleaned

    • kaggle.com
    zip
    Updated Apr 19, 2021
  11. Ethereum Block data

    • kaggle.com
    zip
    Updated Jun 25, 2021
  12. Monthly size of crypto theft 2020-2022

    • statista.com
    Updated Feb 3, 2022
  13. Blockchain Market in Supply Chain Industry Analysis North America, Europe,...

    • technavio.com
    Updated Dec 15, 2023
  14. Ethereum Historical Data

    • kaggle.com
    zip
    Updated Oct 6, 2018
  15. Ethereum Historical Dataset

    • kaggle.com
    zip
    Updated Apr 16, 2020
  16. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Cite
George Azzopardi; Steven Farrugia; Joshua Ellul; Joshua Ellul; George Azzopardi; Steven Farrugia (2021). Detection of illicit accounts over the Ethereum blockchain [Dataset]. http://doi.org/10.34894/GKAQYN

Data from: Detection of illicit accounts over the Ethereum blockchain

Related Article
Explore at:
csv(1016388), txt(506)Available download formats
Dataset updated
Feb 19, 2021
Dataset provided by
DataverseNL
Authors
George Azzopardi; Steven Farrugia; Joshua Ellul; Joshua Ellul; George Azzopardi; Steven Farrugia
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Description

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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