Blockchain analysis is the process of inspecting, identifying, clustering, modeling and visually representing data on a cryptographic distributed-ledger known as a blockchain.[1][2] The goal of blockchain analysis is to discover useful information about different actors transacting in cryptocurrency. Analysis of public blockchains such as Bitcoin and Ethereum is typically conducted by private companies like Arkham Intelligence, Chainalysis, TRM Labs, Elliptic, Nansen, Blockpliance, Elementus, Dune Analytics, CryptoQuant, and Ormi Labs.[3]
Blockchain analysis enables law enforcement to trace cryptocurrencies back to individuals wallets on exchanges, which can then be subpoenaed for information on criminal actors.
Method
Because blockchains are typically public, anyone can view the contents of transactions by querying a node or block explorer site (such as Etherscan.io or BitRef.com). By using common-spend clustering algorithms, it is possible to map the transactions of certain entities on the blockchain.[7] This is how criminals are being caught moving illicit funds using various cryptocurrencies.[8]
Law enforcement and blockchain surveillance
Blockchain analysis has helped produce evidence in several high interest cases.[9] In 2018, an analysis of bitcoin transactions uncovered a link between major cryptocurrency exchange BTC-e and Fancy Bear.[10] In 2019, a major website hosting child sexual abuse material was taken down by law enforcement using blockchain analysis techniques.[11]
Recent academic research highlights significant advances in blockchain analytics, notably:
Real-time monitoring and indexing of on-chain data for detection of anomalies and protocol behavior.[16]
Cross-chain interoperability analytics, addressing scalability and data integration across multiple blockchain platforms.[17]
AI/ML-powered risk detection, leveraging machine learning models to identify suspicious patterns, fraud, and illicit activity in transaction graphs.[18]
These developments reflect a shift from retrospective forensic tools to proactive, automated infrastructure for securing and analyzing blockchain ecosystems.
References
↑ Meiklejohn, Sarah; Pomarole, Marjori; Jordan, Grant; Levchenko, Kirill; McCoy, Damon; Voelker, Geoffrey M.; Savage, Stefan (23 October 2013). "A fistful of bitcoins". Proceedings of the 2013 conference on Internet measurement conference. Imc '13. pp.127–140. doi:10.1145/2504730.2504747. ISBN9781450319539. S2CID2224198.
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