# Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum

arXiv: https://arxiv.org/abs/2308.16391 · HTML: https://mlsanity.com/paper/2308.16391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018)
- [A Survey on the Security of Blockchain Systems](https://mlsanity.com/paper/1802.06993/) (2018)
- [Untangling Blockchain: A Data Processing View of Blockchain Systems](https://mlsanity.com/paper/1708.05665/) (2017)

## Abstract (arXiv)

The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum blockchain, causing considerable financial losses to many crypto investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code. This contract-code-based approach, while achieving very high accuracy, is not robust because a Ponzi developer can fool a detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected. On the contrary, a transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. In this paper, we aim to improve the accuracy of the transaction-based models by employing time-series features, which turn out to be crucial in capturing the life-time behaviour a Ponzi application but were completely overlooked in previous works. We propose a new set of 85 features (22 known account-based and 63 new time-series features), which allows off-the-shelf machine learning algorithms to achieve up to 30% higher F1-scores compared to existing works.