Data Mining for Detecting Bitcoin Ponzi Schemes

@article{Bartoletti2018DataMF,
  title={Data Mining for Detecting Bitcoin Ponzi Schemes},
  author={Massimo Bartoletti and Barbara Pes and Sergio Serusi},
  journal={2018 Crypto Valley Conference on Blockchain Technology (CVCBT)},
  year={2018},
  pages={75-84}
}
Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. [] Key Method 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…

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References

SHOWING 1-10 OF 44 REFERENCES

An inquiry into money laundering tools in the Bitcoin ecosystem

A first systematic account of opportunities and limitations of anti-money laundering (AML) in Bitcoin, a decentralized cryptographic currency proliferating on the Internet, is provided and it appears unlikely that a Know-Your-Customer principle can be enforced in the Bitcoin system.

BitIodine: Extracting Intelligence from the Bitcoin Network

A modular framework, BitIodine, which parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classifies such users and labels them, and finally visualizes complex information extracted from the Bitcoin network is presented.

Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis

This paper proposes a number of features that can be extracted from transactions in Bitcoin that are based upon relating Bitcoin addresses with graph mining procedures and shows that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.

Unsupervised learning for robust Bitcoin fraud detection

This paper investigates the use of trimmed k-means, that is capable of simultaneous clustering of objects and fraud detection in a multivariate setup, to detect fraudulent activity in Bitcoin transactions.

There's No Free Lunch, Even Using Bitcoin: Tracking the Popularity and Profits of Virtual Currency Scams

The first empirical analysis of Bitcoin-based scams: operations established with fraudulent intent is presented, finding that at least $11 million has been contributed to the scams from 13 000 distinct victims.

Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods

Three unsupervised learning methods including k-means clustering, Mahalanobis distance, and Unsupervised Support Vector Machine are used on two graphs generated by the Bitcoin transaction network to detect which users and transactions are the most suspicious.

Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact

The promise and perils of digital currencies

  • T. Moore
  • Mathematics
    Int. J. Crit. Infrastructure Prot.
  • 2013

Behind closed doors: measurement and analysis of CryptoLocker ransoms in Bitcoin

This study performs a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014 and finds evidence that suggests connections to popular Bitcoin services, and subtle links to other cybercrimes surrounding Bitcoin.

SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies

This work identifies three key components of Bit coin's design that can be decoupled, and maps the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools.