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Bitcoin is a peer-to-peer distributed cryptocurrency system, that keeps all transaction history in a public ledger known as blockchain. The Bitcoin network is implicitly pseudoanonymous and its nodes are controlled by independent entities making network analysis difficult. This calls for the development of a fully controlled testing environment. This paper presents Kriptosare.gen, a dockerized automatized Bitcoin testbed, for deploying full-scale custom Bitcoin networks. The testbed is deployed in a single machine executing four different experiments, each one with different network configuration. We perform a cost analysis to investigate how the resources are related with network parameters and provide experimental data quantifying the amount of computational resources needed to run the different types of simulations. Obtained results demonstrate that it is possible to run the testbed with a configuration similar to a real Bitcoin system., Comment: 9 pages, 4 figures, 1 table, presented during the 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)

Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network. In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation. Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities., Comment: 15 pages,7 figures, 4 tables, presented in 2019 IEEE International Conference on Blockchain (Blockchain)