FRAUDULENT ACCOUNT DETECTION IN THE ETHEREUM’S NETWORK USING VARIOUS MACHINE LEARNING TECHNIQUES

Authors

  • Amer Sallam Faculty of Engineering and IT, Taiz University, Yemen
  • Taha Rassem Faculty of Science and Technology, Bournemouth University, United Kingdom
  • Hanadi Abdu Faculty of Engineering and IT, Taiz University, Yemen
  • Haneen Abdulkareem Faculty of Engineering and IT, Taiz University, Yemen
  • Nada Saif Faculty of Engineering and IT, Taiz University, Yemen
  • Samia Abdullah Faculty of Engineering and IT, Taiz University, Yemen

DOI:

https://doi.org/10.15282/ijsecs.8.2.2022.5.0102

Keywords:

Ethereum; Fraudulent Accounts ; Machine Learning

Abstract

On the Ethereum network, users communicate with one another through a variety of different accounts. Pseudo-anonymity was enforced over the network to provide the highest level of privacy. By using accounts that engage in fraudulent activity across the network, such privacy may be exploited. Like other cryptocurrencies, Ethereum blockchain may exploited with several fraudulent activities such as Ponzi schemes, phishing, or Initial Coin Offering (ICO) exits, etc. However, the identification of parameters with abnormal account characteristics is not an easy task and requires an intelligent approach to distinguish between normal and fraudulent activities. Therefore, this paper has attempted to solve this a problem by using machine learning techniques to introduce a robust approach that can detect fraudulent accounts on Ethereum. We have used a K-Nearest Neighbor, Random Forest and XGBoost over a collected dataset of 4,681 instances along with 2,179 fraudulent accounts associated and 2,502 regular accounts. The XGBoost, RF, and KNN techniques achieved average accuracies of 96.80 %, 94.8 8%, and 87.85% and an average AUC of 0.995, 0.99 and 0.93, respectively.

Published

2022-08-30

How to Cite

Sallam, A. ., Rassem, T., Abdu, H., Abdulkareem, H., Saif, N., & Abdullah, S. (2022). FRAUDULENT ACCOUNT DETECTION IN THE ETHEREUM’S NETWORK USING VARIOUS MACHINE LEARNING TECHNIQUES. International Journal of Software Engineering and Computer Systems, 8(2), 43–50. https://doi.org/10.15282/ijsecs.8.2.2022.5.0102