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Fraudulent Account Detection in the Ethereum's Network Using Various Machine Learning Techniques

Amer Sallam, Faculty of Engineering and IT, Taiz University, Yemen, Taha H. Rassem, Hanadi Abdu, Haneen Abdulkareem, Nada Saif, Samia Abdullah, Faculty of Science and Technology, Bournemouth University, United Kingdom, Faculty of Engineering and IT, Taiz University, Yemen, Faculty of Engineering and IT, Taiz University, Yemen, Faculty of Engineering and IT, Taiz University, Yemen, Faculty of Engineering and IT, Taiz University, Yemen
2022 International Journal of Software Engineering and Computer Systems  

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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
more » ... rameters 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.
doi:10.15282/ijsecs.8.2.2022.5.0102 fatcat:qubo4kmce5asrm7bhif2t6ufmu
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Amer Sallam, Faculty of Engineering and IT, Taiz University, Yemen, Taha H. Rassem, Hanadi Abdu, Haneen Abdulkareem, Nada Saif, Samia Abdullah, Faculty of Science and Technology, Bournemouth University, United Kingdom, Faculty of Engineering and IT, Taiz University, Yemen, Faculty of Engineering and IT, Taiz University, Yemen, Faculty of Engineering and IT, Taiz University, Yemen, Faculty of Engineering and IT, Taiz University, Yemen. "Fraudulent Account Detection in the Ethereum's Network Using Various Machine Learning Techniques." International Journal of Software Engineering and Computer Systems 8.2 (2022) 43-50