Explainable Ponzi Schemes Detection on Ethereum

TL;DR

This paper introduces an explainable machine learning classifier for detecting Ponzi scheme smart contracts on Ethereum, supported by a new labeled dataset and feature analysis, improving detection performance and interpretability.

Contribution

It provides a new labeled dataset of Ethereum smart contracts, develops a superior classifier, and applies explainable AI to identify key features influencing detection.

Findings

Classifier outperforms existing methods in AUC

Small feature set achieves high classification quality

Explainable AI reveals impactful features

Abstract

Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious actors who deliver scams to deceive honest users and to gain economic advantages. Ponzi schemes are one of the most common scams. Here, we present a classifier for detecting smart Ponzi contracts on Ethereum, which can be used as the backbone for developing detection tools. First, we release a labelled data set with 4422 unique real-world smart contracts to address the problem of the unavailability of labelled data. Then, we show that our classifier outperforms the ones proposed in the literature when considering the AUC as a metric. Finally, we identify a small and effective set of features that ensures a good classification quality and investigate their impacts on the classification using eXplainable AI techniques.