arXiv subjects

Yekaterina Smolenkova

Publications and source records attributed to Yekaterina Smolenkova.

2 recordsLinked to original sources

arXiv2026

Shedding Light on Complex Bitcoin Mixer Transactions: 67-Fold Reduction in Unclassified Cases

Bitcoin's Unspent Transaction Output (UTXO) model enables public analysis of fund flows, but users often merge transactions into Shared Send Mixers (SSMs) to obscure these flows. Untangling SSMs to recover original subtransactions is an NP-complete problem. While a practical untangling algorithm exists, it fails to classify 1.4% of SSM transactions due to computational time limits. This paper introduces four novel heuristics that exploit structural weaknesses in real-world SSM transactions to resolve these timeout cases: preemptive grouping, connectable singleton, ambiguous pairing, and knapsack fallback. We provide theoretical proofs validating each heuristic and integrate them into an optimized pipeline. Applied to timeout transactions, our approach classifies 98.5% of previously unresolved cases, reducing the overall unclassified transaction rate from 1.4% to 0.021% of all SSM transactions. Our open-source implementation and comprehensive evaluation on the complete Bitcoin blockchain demonstrate that the heuristics effectively untangle previously intractable transactions, enabling more accurate flow analysis and deeper structural insights into cryptocurrency transaction patterns.

cs.CR↗

arXiv2026

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

Detecting illicit cryptocurrency transactions is hampered by extreme class imbalance, adversarial obfuscation, and a scarcity of reliable labels. While semi-supervised learning (SSL) offers a promising solution by leveraging unlabeled data, we show that its success is not guaranteed by data volume alone but is contingent on data quality. We introduce an SSL framework for detecting illicit Bitcoin flows in Shared Send Mixers (SSM) transactions, built on a comprehensive historical dataset comprising 163 million transactions. Our main conclusion is that the success of SSL depends on data quality rather than volume: high-fidelity features such as KeyLinker address clustering and Shared Send Untangling (SSU) complexity metrics achieve an F1 score of 0.84 on unlabeled data. Finally, we empirically show that common heuristics like One-Time Change (OTC), though abundant, introduce noise, while strategic reliance on higher-fidelity features like KeyLinker is essential. Our work establishes that in blockchain forensics, the path to better performance lies in smarter feature engineering for data quality, not just larger datasets.

cs.LG↗