Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162852
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dc.contributor.authorAng, Guang Yaoen_US
dc.date.accessioned2022-11-11T02:30:18Z-
dc.date.available2022-11-11T02:30:18Z-
dc.date.issued2022-
dc.identifier.citationAng, G. Y. (2022). Ethereum smart contract exploitation detection using machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162852en_US
dc.identifier.urihttps://hdl.handle.net/10356/162852-
dc.description.abstractVulnerabilities in Ethereum smart contracts may be exploited by malicious actors for financial gains. While many vulnerability detection tools are available, these tools are not perfect and vulnerable smart contracts may still be deployed into the Ethereum blockchain. As such, the detection and identification of malicious transactions becomes important for contract owners and the community. In this project, we propose the use of anomaly detection machine learning algorithms to detect malicious transactions based on information recorded on the blockchain. Malicious transactions are considered anomalies and are generally uncommon as compared to benign transactions. By grouping existing smart contracts of similar functionalities, we can build a machine learning model using historical transactions information from these smart contracts and apply it to detect future malicious transactions. We will also evaluate the effectiveness of our approach on past exploitations in the Ethereum main net and present the results.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21-0757en_US
dc.subjectEngineering::Computer science and engineering::Softwareen_US
dc.titleEthereum smart contract exploitation detection using machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Shang-Weien_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailshang-wei.lin@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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