# Do not rug on me: Zero-dimensional Scam Detection

arXiv: https://arxiv.org/abs/2201.07220 · HTML: https://mlsanity.com/paper/2201.07220/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 16 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [SoK: Decentralized Finance (DeFi)](https://mlsanity.com/paper/2101.08778/) (2021)
- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts](https://mlsanity.com/paper/1902.06976/) (2019)
- [An analysis of Uniswap markets](https://mlsanity.com/paper/1911.03380/) (2019)
- [Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges](https://mlsanity.com/paper/1904.05234/) (2019)
- [Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph](https://mlsanity.com/paper/1912.10105/) (2019)
- [High-Frequency Trading on Decentralized On-Chain Exchanges](https://mlsanity.com/paper/2009.14021/) (2020)
- [Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks](https://mlsanity.com/paper/2106.01806/) (2021)
- [SoK: Oracles from the Ground Truth to Market Manipulation](https://mlsanity.com/paper/2106.00667/) (2021)
- [Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites](https://mlsanity.com/paper/2005.14440/) (2020)
- [Clockwork Finance: Automated Analysis of Economic Security in Smart Contracts](https://mlsanity.com/paper/2109.04347/) (2021)
- [Don't Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams](https://mlsanity.com/paper/2007.13639/) (2020)

## Built on by (most cited first, 3 of 3)

- [SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance](https://mlsanity.com/paper/2504.07132/) (2025)
- [MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection](https://mlsanity.com/paper/2602.13480/) (2026)
- [CARE: A Lightweight Plug-in Gated Correction and Uncertainty-aware Module for Long-term Time Series Forecasting](https://mlsanity.com/paper/2610.11165/) (2026)

## Abstract (arXiv)

Uniswap, like 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 makes 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 existed in traditional finance but has become more relevant in DeFi. Various projects such as [34,37] 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 in [44]. 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 data set 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 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 an 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.