Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain
This addresses the challenge of detecting illicit behavior like ransomware and darknet market transactions for cryptocurrency analysts, offering a more efficient and interpretable solution compared to existing methods.
The paper tackles the problem of detecting illicit activities in Bitcoin transactions by introducing Chainlet Orbits, an address embedding method based on topological characteristics, which achieves superior performance in node classification experiments compared to state-of-the-art methods and enables interpretable models in as little as 15 minutes.
The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets. These illegal activities often utilize Bitcoin as the preferred payment method. However, current tools for detecting illicit behavior either rely on a few heuristics and laborious data collection processes or employ computationally inefficient graph neural network (GNN) models that are challenging to interpret. To overcome the computational and interpretability limitations of existing techniques, we introduce an effective solution called Chainlet Orbits. This approach embeds Bitcoin addresses by leveraging their topological characteristics in transactions. By employing our innovative address embedding, we investigate e-crime in Bitcoin networks by focusing on distinctive substructures that arise from illicit behavior. The results of our node classification experiments demonstrate superior performance compared to state-of-the-art methods, including both topological and GNN-based approaches. Moreover, our approach enables the use of interpretable and explainable machine learning models in as little as 15 minutes for most days on the Bitcoin transaction network.