BitIodine: Extracting Intelligence from the Bitcoin Network

By Michele Spagnuolo, F. Maggi, Stefano Zanero. Financial Cryptography, 2014

323 citations recorded by the source corpus.

Bitcoin, the famous peer-to-peer, decentralized electronic currency system, allows users to benefit from pseudonymity, by generating an arbitrary number of aliases (or addresses) to move funds. However, the complete history of all transactions ever performed, called “blockchain”, is public and replicated on each node. The data it contains is difficult to analyze manually, but can yield a high number of relevant information. In this paper we present 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. BitIodine labels users semi-automatically with information on their identity and actions which is automatically scraped from openly available information sources. BitIodine also supports manual investigation by finding paths and reverse paths between addresses or users. We tested BitIodine on several real-world use cases, identified an address likely to belong to the encrypted Silk Road cold wallet, or investigated the CryptoLocker ransomware and accurately quantified the number of ransoms paid, as well as information about the victims. We release a prototype of BitIodine as a library for building Bitcoin forensic analysis tools.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/1ab6010bdfa4d15e6931d85b1f63579a2424f608

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Citations in the corpus

Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks (2015, methodology): “The number of Bitcoins collected by cybercriminals during Cryptolocker attack is previously reported [35].”
CoinShuffle: Practical Decentralized Coin Mixing for Bitcoin (2014, unknown): “Meiklejohn et al. [9] demonstrate how to employ a few basic heuristics to classify Bitcoin addresses that are likely to belong to the same user; this is further refined by Spagnuolo, Maggi, and Zanero [10].”
Social media big data analytics: A survey (2019, unknown): “Moreover, Spagnuolo, Maggi, and Zanero (2014) used Bitcoin OTC trust-weighted signed network datasets, which allows the monitoring of the Bitcoin economy through tracing users' money using attached identities.”
A Survey on Security and Privacy Issues of Bitcoin (2017, methodology): “A commercial approach for blockchain analysis could be to use the software BitIodine [115] that offers an automated blockchain analysis framework.”