# Data mining for detecting Bitcoin Ponzi schemes

arXiv: https://arxiv.org/abs/1803.00646 · HTML: https://mlsanity.com/paper/1803.00646/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 17 of 17)

- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis](https://mlsanity.com/paper/2204.05746/) (2022)
- [Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains](https://mlsanity.com/paper/2101.11915/) (2021)
- [Towards Malicious address identification in Bitcoin](https://mlsanity.com/paper/2112.11721/) (2021)
- [Pattern Analysis of Money Flow in the Bitcoin Blockchain](https://mlsanity.com/paper/2207.07315/) (2022)
- [Explainable Ponzi Schemes Detection on Ethereum](https://mlsanity.com/paper/2301.04872/) (2023)
- [Data-driven Smart Ponzi Scheme Detection](https://mlsanity.com/paper/2108.09305/) (2021)
- [An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams](https://mlsanity.com/paper/2210.14408/) (2022)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [SourceP: Detecting Ponzi Schemes on Ethereum with Source Code](https://mlsanity.com/paper/2306.01665/) (2023)
- [From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency](https://mlsanity.com/paper/2309.15133/) (2023)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [A Bytecode-based Approach for Smart Contract Classification](https://mlsanity.com/paper/2106.15497/) (2021)
- [Toward Intention Discovery for Early Malice Detection in Bitcoin](https://mlsanity.com/paper/2209.12001/) (2022)
- [Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum](https://mlsanity.com/paper/2308.16391/) (2023)
- [Cryptocurrency Frauds for Dummies: How ChatGPT introduces us to fraud?](https://mlsanity.com/paper/2406.03079/) (2024)
- [Clean Up the Mess: Addressing Data Pollution in Cryptocurrency Abuse Reporting Services](https://mlsanity.com/paper/2410.21041/) (2024)

## Abstract (arXiv)

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.