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Discovering Bitcoin Mixing Using Anomaly Detection

2018; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-75193-1_64

1611-3349

Mario Alfonso Prado-Romero, Christian Doerr, Andrés Gago-Alonso,

Data Stream Mining Techniques

Bitcoin is a peer-to-peer electronic currency system which has increased in popularity in recent years, having a market capitalization of billions of dollars. Due to the alleged anonymity of the Bitcoin ecosystem, it has attracted the attention of criminals. Mixing services are intended to provide further anonymity to the Bitcoin network, making it impossible to link the sender of some money with the receiver. These services can be used for money laundering or to finance terrorist groups without being detected. We propose to model the Bitcoin network as a social network and to use community anomaly detection to discover mixing accounts. Furthermore, we present the first technique for detecting Bitcoin accounts associated to money mixing, and demonstrate our proposal effectiveness on real data, using known mixing accounts.