This research introduces a novel LLM-based approach to link cryptocurrency attribution tags to knowledge graph entities, significantly improving forensic accuracy.

• Outperforms baseline methods by up to 37.4% in F1 score
• Creates consistent connections between tags and well-defined knowledge graph concepts
• Reduces the risk of misleading investigations and false accusations
• Establishes a foundation for more reliable cryptoasset forensics

The security implications are substantial, as improved tag accuracy directly enhances digital forensic investigations and strengthens cybersecurity measures in cryptocurrency monitoring.

Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach