The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Do Not Rug on Me: Leveraging Machine Learning Techniques for Automated Scam Detection
Open accessAbstract
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.
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“This is a level of dysfunction and economic instability hard to countenance for experimentation in the developing world, made worse by the fact that recent research has identi fi ed that 97.7% of tokens listed on the largest DeFi exchange, Uniswap, were rugpulls (Mazorra et al., 2022: 20).”
From Blockchain financialization, neo-colonialism, and Binance (2023) · cites this paper as Mazorra et al., 2022
Citations
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Larian Studios Dev Team (2024) · doi:10.1145/3292500.3330701 · Cited by 8,618
Intensity and coherence of motifs in weighted complex networks.
Onnela, J., Saramäki, J., Kertész, J., & Kaski, K. (2004) · Physical review. E, Statistical, nonlinear, and soft matter physics · doi:10.1103/physreve.71.065103 · Cited by 1,087
Slither: A Static Analysis Framework for Smart Contracts
Feist, J., Grieco, G., & Groce, A. (2019) · doi:10.1109/wetseb.2019.00008 · Cited by 856
Introducing Ethereum and Solidity
Dannen, C. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2535-6 · Cited by 785
Daian, P., Goldfeder, S., Kell, T., Li, Y., Zhao, X., Bentov, I., Breidenbach, L., & Juels, A. (2020) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40000.2020.00040 · Cited by 694
SoK: Decentralized Finance (DeFi)
Werner, S. M., Pérez, D., Gudgeon, L., Klages‐Mundt, A., Harz, D., & Knottenbelt, W. J. (2022) · ACM Conference on Advances in Financial Technologies (AFT) · doi:10.1145/3558535.3559780 · Cited by 443
Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding
Wu, J., Yuan, Q., Lin, D., You, W., Chen, W., Chen, C., & Zheng, Z. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3016821 · Cited by 422
Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact
Bartoletti, M., Carta, S., Cimoli, T., & Saia, R. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.014 · Cited by 404
Research works printed but not linked: 4
- Nakamoto, S. (2019). Bitcoin: A Peer-to-Peer Electronic Cash System, SSRN. Technical Report.
- Xia, P., Gao, B., Su, W., Yu, Z., Luo, X., Zhang, C., Xiao, X., and Xu, G. (2021). Demystifying Scam Tokens on Uniswap Decentralized Exchange. arXiv.
- Babel, K., Daian, P., Kelkar, M., and Juels, A. (2021). Clockwork Finance: Automated Analysis of Economic Security in Smart Contracts. arXiv.
- Adams, 2020, Uniswap v2 Core Whitepaper, Retrieved Oct., 12, 36
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Metadata
- Authors
- Mazorra, B., Adan, V., & Daza, V.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Universitat de Barcelona (ES)
- Corresponding author
- Daza, Vanesa
- Venue
- Mathematics
- Publisher
- MDPI AG
- Published
- 2022-03-16
- Volume
- 10
- Issue
- 6
- Pages
- 949
- ISSN
- 2227-7390 (ISSN-L)
- OpenAlex
- W4220664925
- Cited by
- 64
- References
- 33
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 17 · 2023: 13 · 2022: 6
- FWCI
- 13.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Mathematics (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Funders
- European Commission (814284, HORIZON2020)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Law › Cybercrime and Law Enforcement Studies
- Keywords
- Uniswap, decentralized exchanges
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