Abstract
The Ethereum blockchain has been subject to an increasing fraudulent activity during the recent years that hinders its democratization. To detect fraudulent accounts, previous works have exploited supervised machine learning algorithms. We can identify two main approaches. The first approach consists in representing transaction records as a graph in order to apply node embedding algorithms. The second approach consists in calculating statistics based on the amount and time of transactions realized. The former approach leads to better results at this day. However, transactional data approaches - based on time and data only - are not used to their full potential. This paper adopts a transactional data approach by expanding feature calculation to every transaction properties. We study three classification models: XGBoost, SVM Classifier and Logistic Regression and operate a feature selection protocol to highlight the most significant features. Our model results in a 26 features dataset providing an f-score of 0.9654.
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Bella Baci, A., Brousmiche, K., Amal, I., Abdelhédi, F., Rigaud, L. (2023). Detecting Illicit Ethereum Accounts Based on Their Transaction History and Properties and Using Machine Learning. In: Awan, I., Younas, M., Bentahar, J., Benbernou, S. (eds) The International Conference on Deep Learning, Big Data and Blockchain (DBB 2022). DBB 2022. Lecture Notes in Networks and Systems, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-031-16035-6_8
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DOI: https://doi.org/10.1007/978-3-031-16035-6_8
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