Cryptocurrencies Forensics With Real-Time intelligence and Graph Database: a Comprehensive Review
Bibliographic record
Abstract
The advent of cryptocurrencies has paved the way for an alternative financial system based on blockchain technology. The properties of this technology allow untrustworthy entities to exchange transactions with each other, confidentially, transparently and anonymously. This latter property combined to the solutions for mixing cryptocurrency transactions has contributed significantly to strengthening the anonymization of cryptocurrencies. Consequently, they have facilitated illegal activities. Nowadays, the majority of financial crimes are carried out using cryptocurrencies. To mitigate this problem, cryptocurrencies forensics techniques have been suggested with the aim of reducing illicit financial activities. However, most of these existing approaches do not support real-time processing, do not offer architecture for Big Data processing. Moreover, these methods do not provide an optimized data storage system based on a graph database. This paper performs a comprehensive investigation into cryptocurrencies forensics techniques including real-time big data processing and graph database storage. It explores and classifies existing cryptocurrencies forensics techniques to effectively support decision making for their implementation. Furthermore, it reviews recent research effort to enforce graph data storage system, big data and real-time processing while executing cryptocurrencies forensics. Through this paper, we aim to clear up the outlook by suggesting an outline of the different method, tools and application when raising the basic differences and presenting associated advantages and limitations. More importantly, we underline current challenges and future research directions to stimulate research in cryptocurrencies forensics with real-time and big data processing using graph data storage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".