Mixers Detection in bitcoin network: a step towards detecting money laundering in crypto-currencies
Why this work is in the frame
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Bibliographic record
Abstract
Anonymity is one of major factors that is causing the rise of bitcoin crypto-currency. There are several attacks (positive or negative) to de-anonymize the bitcoin addresses, in order to link the bitcoin entity to a physical entity or person. Bitcoin mixing service (called mixer) is one of the approaches to keep the user’s crpto-anonymity in the transparent ledger of bitcoin network. Mixers breaks the link between the sender and the receiver by mixing up coins received from multiple sources, while creating a mess to make it impossible to identify the actual sender of bitcoins. On the other hand, mixing services are being vastly exploited by criminals for laundering the illegal money, taken from frauds, ransom, scams, or other illegal activities. Detecting mixing services or mixer’s involvement in a transaction can help in discovering money laundering activities in the bitcoin blockchain. Existing mixer’s detection approaches either have a low accuracy-rate due to the changing nature of the mixing process or they are not efficient enough to be implemented in a real-time environment. In this paper, we developed a highly accurate decision-tree based model using C4.5 machine learning approach to identify addresses providing mixing services. To make this detection process efficient and be able to work in a real environment, we reduced overall feature-set to only eight features, minimizing overall computation time. Further, we shrink the decision-tree using reduced error-pruning to make the detection process faster. With the short decision-tree-size of 55 nodes, we achieved the accuracy of more than 97%, which is quite higher.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it