GuiltyWalker: Distance to illicit nodes in the Bitcoin network
This addresses the problem of illicit transaction detection for cryptocurrency authorities and users, but it is incremental as it builds on existing methods.
The paper tackled detecting money laundering in Bitcoin transactions by proposing new graph-based features that measure distance to illicit nodes, which improved illicit classification by about 5 percentage points, especially during black market shutdowns.
Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities and their anonymity. Thus, it is important to develop new techniques to detect and prevent illicit cryptocurrency transactions. In our work, we propose new features based on the structure of the graph and past labels to boost the performance of machine learning methods to detect money laundering. Our method, GuiltyWalker, performs random walks on the bitcoin transaction graph and computes features based on the distance to illicit transactions. We combine these new features with features proposed by Weber et al. and observe an improvement of about 5pp regarding illicit classification. Namely, we observe that our proposed features are particularly helpful during a black market shutdown, where the algorithm by Weber et al. was low performing.