Detecting Fraudulent Transactions in Ethereum Blockchain via Machine Learning Classification

Weber, Camila (2023) Detecting Fraudulent Transactions in Ethereum Blockchain via Machine Learning Classification. Masters thesis, Dublin, National College of Ireland.

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Abstract

Blockchain is a network that has grown exponentially in recent years, it is a decentralized technology and for this reason it requires the development of secure applications and a better understanding of this networking. The project focuses on creating a classification model with a secure approach towards the Ethereum network, as it is the second most important cryptocurrency in the Blockchain domain. Implementing Machine Learning methods, the aim of the project is to investigate and detect possible fraud within Ethereum. Exploring the concepts of classification methods in Machine Learning, and developing important models that reinforce security in these decentralized networks. By analysing the data and answering the research question of the project, Machine Learning models were applied such as the Support Vector Machine, Logistic Regression and Random Forest for effective detection of fraud in transactions on the Ethereum network. This research still presents significant studies and methodologies that lead insights for future projects.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Milosavljevic, Vladimir UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security > Database security > Blockchains (Databases) T Technology > T Technology (General) > Information Technology > Computer software > Computer Security > Database security > Blockchains (Databases) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources > Databases > Distributed databases > Blockchains (Databases) H Social Sciences > HG Finance > Money > Digital currency > Cryptocurrencies Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	10 Jan 2025 16:44
Last Modified:	10 Jan 2025 16:44
URI:	https://norma.ncirl.ie/id/eprint/7305

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