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arXiv Machine Learning By Yekaterina Smolenkova, Nickolay Larionov, Nikolay Ivanov, Yury Yanovich

Quality over Quantity: Semi-Supervised Detection of Illicit Bitcoin Flows via Feature Engineering

Read the original on arXiv Machine Learning →

The paper presents a semi‑supervised learning framework for detecting illicit Bitcoin flows in Shared Send Mixers, using a large historical dataset of 163 million transactions. It demonstrates that the success of SSL depends on data quality rather than sheer volume, with high‑fidelity features such as KeyLinker address clustering and Shared Send Untangling complexity metrics achieving an F1 score of 0.84 on unlabeled data. The study also shows that common heuristics like One‑Time Change introduce noise, underscoring the importance of smarter feature engineering in blockchain forensics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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