Cards, Money and Two Hacking Forums: An Analysis of Online Money Laundering Schemes
Bibliographic record
Abstract
The emergence of the internet as a global, borderless communication platform afforded a wide range ofsocial and economic opportunities to people throughout theworld. Criminals have exploited the ability to communicateinstantaneously around the globe to facilitate crossjurisdictionalcyber-fraud and subsequently, online moneylaundering. Coordinating international fraud and moneylaundering schemes requires a medium of communication, suchas online hacking and carding forums, where offenders meet toexchange information and to engage in their illegal business. Forthe study presented in this paper, publicly available onlinecarding and hacking forums were downloaded and keywords ofinterest pertaining to online money laundering were extracted. This study undertakes an analysis of two large Russian-speakinghacking and carding forums by qualitatively analyzing andquantifying contexts of keyword usage. Findings indicate thatcyber-fraudsters are primarily interested in cashing outdigitally stolen funds and do so mainly by resorting to theservices of money mules and virtual casinos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".