Vers une meilleure identification d'acteurs de Bitcoin par apprentissage supervisé
Abstract
Bitcoin is the most used and studied cryptocurrency. Being decentralized, transaction data
is freely accessible and can thus be analyzed. The first step to most analyses consists in grouping
individual – anonymous – addresses in clusters, corresponding to Bitcoin actors. In this
article, we propose a new method to realize these machine learning-based aggregates. Our
approach is based on the construction of a training dataset whose class variable is obtained by
a ground truth computed a posteriori. This dataset is used to identify the change addresses of
transactions, addresses belonging to the author of the transaction. This makes it possible to
increase the number of addresses discovered as belonging to the same actor. Using an external
validation criterion, we experimentally show the relevance of this method in comparison with
the heuristics usually used.