Do not rug on me

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• dc.contributor.author Mazorra, Bruno
• dc.contributor.author Adan, Victor
• dc.contributor.author Daza, Vanesa
• dc.date.accessioned 2023-02-23T07:09:00Z
• dc.date.available 2023-02-23T07:09:00Z
• dc.date.issued 2022
• dc.description.abstract Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.
• dc.description.sponsorship This paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 814284.
• dc.format.mimetype application/pdf
• dc.identifier.citation Mazorra B, Adan V, Daza V. Do not rug on me: leveraging machine learning techniques for automated scam detection. Mathematics. 2022;10(6):949. DOI: 10.3390/math10060949
• dc.identifier.doi http://dx.doi.org/10.3390/math10060949
• dc.identifier.issn 2227-7390
• dc.identifier.uri http://hdl.handle.net/10230/55881
• dc.language.iso eng
• dc.publisher MDPI
• dc.relation.ispartof Mathematics. 2022;10(6):949.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/814284
• dc.rights © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword ethereum
• dc.subject.keyword DeFi
• dc.subject.keyword Dex
• dc.subject.keyword scam detection
• dc.title Do not rug on me
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion