Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum

TL;DR

This paper enhances transaction-based Ponzi scheme detection on Ethereum by introducing 85 novel features, including time-series data, significantly improving detection accuracy over previous models.

Contribution

It proposes a new feature set with 63 time-series features for transaction-based detection, boosting F1-score by up to 30%.

Findings

Achieved up to 30% higher F1-scores with new features.

Time-series features are crucial for capturing Ponzi scheme behavior.

Transaction-based detection can be more robust than code-based methods.

Abstract

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.