Kriptosare: Behavior Analysis in Cryptocurrency Transactions

Abstract

Despite being backed by blockchain technology that promises security, immutability, and full transparency, some cryptocurrencies such as Bitcoin have been used as enablers for many licit and illicit activities such as money laundering, terrorism financing, and ransomware payments. In this scenario, the analysis of the transactions, as well as the entities that have generated them, became a crucial step for law enforcement officer (LEO) investigations. However, the (pseudo) anonymity of the network, the lack of regulatory authority, the employment of anonymizer mechanisms, the evolution of entities’ behavior, and the emergence of new dynamics are just five of the main elements that make this task challenging. At the same time, the huge amount of information to be analyzed can result in a waste of time and resources, slowing the investigations. For this reason, in this work, we present Kriptosare, a tool able to classify entity behaviors belonging to Bitcoin, Bitcoin Cash, and Litecoin. On the one hand, the tool makes use of state-of-the-art machine learning techniques to reduce anonymity in the considered cryptocurrencies. This model extracts behaviors from interactions and dynamics of different known entities involved in the transactions and then predicts the behaviors of new unseen entities. On the other hand, Kriptosare includes a crypto simulator able to create and control a private Bitcoin, Bitcoin Cash, or Litecoin network. This unit allows the simulation of crypto transactions in a controlled way for evaluating hypotheses and/or enriching the input data. The presented tool can be used by LEOs to search and highlight the most important red flag indicators that could suggest criminal behavior, and to support their analysis by optimizing their investigation resources.