Moreover, a focus is placed on the application of these techniques to the modern day threat of ransomware, a lucrative branch of contemporary global crime which in 2020 is estimated to cost companies anywhere between $US 42 billion and $US 170 billion worldwide in ransoms paid, lost productivity and other recovery expenses ( Emsisoft, 2020). Furthermore, Machine Learning (ML) and Artificial Intelligence (AI) techniques applied to money laundering, cybercrime and other illicit activities across the Bitcoin ecosystem are reviewed. We then highlight the application of graph analysis techniques to the Bitcoin ecosystem and transaction networks. Following that is a review of the research into the techniques that exploit heuristics and behaviors inherent in the Bitcoin system. Therefore, we first examine the body of literature relating to regulatory efforts that aim to balance the freedom of an open system with the requirements of crime prevention and law enforcement. Many of the techniques wrestle with the problem of attribution in the face of the anonymity of sources within the Bitcoin ecosystem.
These illicit Bitcoin transactions could take the form of money laundering, terrorism financing or the movement of proceeds from other crimes such as ransomware attacks. This paper examines the current literature on the analysis of illicit Bitcoin transactions and focuses specifically on the analytic techniques that are applied to blockchain data.
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