Catching Cyber Criminals: A Multi-Tier Approach to Crypto Crime Attribution

Recent times have seen a dramatic rise in cyber crime. From this, cryptocurrencies have emerged as the preferred medium for illicit payments. Several entities are involved in the attribution of such transactions, although they rely largely on manual efforts from a restricted data space. As a result, crypto crime attribution has become crippled by a data volume crisis. It requires novel, automated approaches derived from cutting-edge technologies to keep up with the rapid onset of cyber crime.

Along with academic professionals from the University of Queensland, Griffith and the University of Melbourne, we take part in developing a world-first machine learning-driven architecture to automatically re-establish the provenance of illicit cryptocurrency transactions. Our project, which is in its fourth year and nearing completion, has attracted attention from Australia’s Office of National Intelligence, AFP and AUSTRAC.

This first-of-its-kind architecture sets a solid foundation for attributing crypto crime from a wide range of data sources. Furthermore, it proposes novel solutions to the long-standing issues experienced by law enforcement including data identification, collection, analysis and presentation. In this way, our project provides a strong foundation in crypto crime attribution and paves the way for collaboration and research.