Analysis of a Cryptocurrency Investment Scam: Pig Butchering
Bibliographic record
Abstract
This paper analyses and investigates a cryptocurrency investment scam involving the suspicious and fraudulent cryptocurrency trading platform, Elite-Bit, through a detailed case study of a victim's experience. With the rapid rise of cryptocurrency, deceptive platforms like Elite-Bit exploit unsuspecting investors by presenting a façade of legitimacy. This case study chronicles the victim's journey, beginning with a seemingly romantic connection through a dating platform, to an introduction to an investment opportunity, and subsequently a financial loss. After investing a substantial amount, the victim faced unexpected barriers when attempting to withdraw funds, including exorbitant transaction fees and other fabricated costs. The analysis reveals how Elite-Bit employs manipulative tactics such as social engineering and false urgency to maintain control over investors, ultimately leading to significant financial loss. These manipulative tactics are referred to as pig butchering. The paper utilises qualitative data from interviews and correspondence with the victim, along with an examination of platform behaviours to highlight common patterns in cryptocurrency scams. An on-chain and off-chain analysis was conducted using the limited input data provided by the victim. To contextualise the collected information, a link analysis was done, utilising the tool Maltego. The link analysis visually maps the entities associated with the suspect within a network of nodes and connections. By situating the Elite-Bit case within the broader context of cryptocurrency regulation and consumer protection, this paper underscores the urgent need for enhanced regulatory frameworks and public awareness initiatives. This study aims to contribute to the ongoing discourse on financial fraud in the cryptocurrency sector, providing insights that may assist in the prevention of future scams and the promotion of more secure investment and trading practices.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".