Date of Award


Document Type


Degree Name

Master of Science in Business Analytics MS-BA


Information Systems, Supply Chain, and Analytics

Committee Chair

Hieu Pham

Committee Member

Yi Tan

Committee Member

Qingyun Zhu


Computer fraud--Detection, Deep learning (Machine learning), Ethereum (Databases)


Detecting fraudulent transactions on the Ethereum network can help cryptocurrency companies that operate on the Ethereum platform protect their users from exposure to fraudsters. The most common fraudulent activities in the cryptocurrency network include phishing and smart Ponzi schemes. Since cryptocurrency technology is still young, most investors lack knowledge of how the smart contacts used in the Ethereum platform operate; hence, they cannot evaluate the risks they are exposed to when carrying out cryptocurrency transactions. The key role that this paper looks at is the application of graph neural networks in the extraction of features of users in the Ethereum platform and their respective transactions to classify them as either fraudulent or not fraudulent. The classification results produced by this research show that application of graph neural networks can be used to detect fraudulent transactions and help managers of the Ethereum platform take the necessary actions toward curbing fraudulent activities.



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