Discovering Bitcoin Mixing Using Anomaly Detection

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.

[1]  Yizhou Sun,et al.  On community outliers and their efficient detection in information networks , 2010, KDD.

[2]  Emmanuel Müller,et al.  Focused clustering and outlier detection in large attributed graphs , 2014, KDD.

[3]  Jeremy Clark,et al.  SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies , 2015, 2015 IEEE Symposium on Security and Privacy.

[4]  Danai Koutra,et al.  Graph based anomaly detection and description: a survey , 2014, Data Mining and Knowledge Discovery.

[5]  Nicolas Christin,et al.  Traveling the silk road: a measurement analysis of a large anonymous online marketplace , 2012, WWW.

[6]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[7]  Fergal Reid,et al.  An Analysis of Anonymity in the Bitcoin System , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[8]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[9]  Malte Möser,et al.  An inquiry into money laundering tools in the Bitcoin ecosystem , 2013, 2013 APWG eCrime Researchers Summit.

[10]  Klemens Böhm,et al.  Ranking outlier nodes in subspaces of attributed graphs , 2013, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW).

[11]  Stefano Zanero,et al.  BitIodine: Extracting Intelligence from the Bitcoin Network , 2014, Financial Cryptography.

[12]  S Iglesias.  Ranking Outlier Nodes in Subspaces of Attributed Graphs , 2013 .

[13]  Rainer Böhme,et al.  Towards Risk Scoring of Bitcoin Transactions , 2014, Financial Cryptography Workshops.

[14]  Andrés Gago Alonso,et al.  Detecting contextual collective anomalies at a Glance , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[15]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.