Data mining for detecting Bitcoin Ponzi schemes

TL;DR

This paper presents a data mining approach using machine learning to detect Bitcoin Ponzi schemes by analyzing blockchain transaction features, achieving high accuracy with low false positives.

Contribution

It introduces a novel dataset of Ponzi scheme features and evaluates multiple machine learning algorithms for effective detection on the Bitcoin blockchain.

Findings

High detection accuracy of Ponzi schemes

Low false positive rate in classification

Effective use of blockchain transaction features

Abstract

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.