Explainable Ponzi Schemes Detection on Ethereum
This work addresses the detection of scams for blockchain users, but it is incremental as it builds on prior detection methods with improved performance.
The authors tackled the problem of detecting Ponzi schemes on Ethereum by releasing a labeled dataset of 4422 smart contracts and developing a classifier that outperforms existing methods in terms of AUC.
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.