Data mining for detecting Bitcoin Ponzi schemes (1803.00646v1)

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.

• The paper applies data mining and machine learning, specifically Random Forest on a dataset of Bitcoin addresses, to effectively detect Ponzi schemes.
• Results show that cost-sensitive Random Forest effectively identifies Ponzi schemes, achieving a high recall of 0.969 with a low false positive rate.
• The methodology offers a scalable approach for regulators to identify fraudulent schemes on the blockchain and can be adapted for other cryptocurrencies and types of fraud.

Data Mining for Detecting Bitcoin Ponzi Schemes

The paper "Data mining for detecting Bitcoin Ponzi schemes" presents a comprehensive examination of the application of machine learning techniques to identify fraudulent Ponzi schemes within the Bitcoin ecosystem. This scholarly work, authored by Massimo Bartoletti, Barbara Pes, and Sergio Serusi from the University of Cagliari, explores the complexities and methodologies for detecting financial fraud masquerading as high-yield investment programs on the blockchain.

Core Concepts and Methodology

Bitcoin, a decentralized cryptocurrency, allows pseudonymous transactions, making it vulnerable to exploitation by cybercriminals. Ponzi schemes, a common fraud where early investors are paid back using the funds of new investors, have proliferated on Bitcoin due to its pseudonymity. The authors aim to leverage data mining techniques to monitor and analyze Bitcoin transactions, constructing a dataset based on real-world Ponzi schemes.

Dataset Construction: The paper describes a meticulous process for gathering Bitcoin addresses used by Ponzi schemes, primarily through manual searches across online forums and blockchain-related websites. Utilizing clustering techniques, specifically the multi-input heuristic, the authors further expand this dataset by identifying linked addresses. The clusters reveal that many schemes operate across a multitude of addresses, further underscoring the complexity of tracking fraudulent activities.

Features and Classification: Subsequent steps involve defining a robust set of features pertinent to Bitcoin addresses. These include characteristics such as lifetime, transaction volume, Gini coefficient of transferred values, and activity metrics. The authors employ these features to train various machine learning models, experimenting with classifiers like RIPPER, Bayes Net, and Random Forest.

Results and Analysis

The paper's most promising findings arise from employing Random Forest in a cost-sensitive learning approach. This configuration yields a classifier capable of identifying 31 Ponzi schemes with commendable recall and specificity metrics, achieving a recall of 0.969. The false positive rate remains low, illustrating the model's efficacy in discerning fraudulent clusters amidst legitimate transactions.

Implications and Future Work

The implications of this research extend into both practical and theoretical realms. Practically, it offers a scalable method for regulatory bodies and surveillance authorities to identify fraudulent schemes on the blockchain, potentially reducing the economic impact of such crimes. Theoretically, the methodology could be adapted for other cryptocurrencies like Ethereum or for different types of financial frauds, providing a foundation for broader applications in cybercrime detection.

Future developments in AI and machine learning could refine these models' accuracy and efficiency, especially as Bitcoin transaction volumes continue to grow. Automated validation of false positives and exploratory analyses using auxiliary data sources such as web forums could further enhance fraud detection capabilities. Additionally, while the paper focuses on detection, exploring mitigation and intervention strategies post-detection would be a valuable extension of this work.

Conclusion

This paper contributes significantly to the dialogue on cryptocurrency fraud detection, offering valuable insights into the use of data-driven techniques for identifying Ponzi schemes. Despite the formidable challenges posed by the pseudonymous nature of Bitcoin, the authors demonstrate the potential of machine learning to provide effective solutions, paving the way for continued research and development in this crucial area.