Cryptocurrencies Emerging Threats and Defensive Mechanisms: A Systematic Literature Review
Why this work is in the frame
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Bibliographic record
Abstract
Cryptocurrencies have been a target for cybercriminal activities because of the pseudo-anonymity and privacy they offer. Researchers have been actively working on analyzing and developing innovative defensive mechanisms to prevent these activities. A significant challenge facing researchers is collecting datasets to train defensive systems to detect and analyze these cyberattacks. Our aims in this systematic review are to explore and aggregate the state of the art threats that have emerged with cryptocurrencies and the defensive mechanisms that have been proposed. We also discuss the threats type, scale, and how efficient the defensive mechanisms are in providing early detection and prevention. We also list out the resources that have been used to collect datasets, and we identify the publicly available ones. In this study, we extracted 1,221 articles from four top scientific and engineering databases and libraries in Computer Science: IEEE Xplore, ACM Digital Library, Elsevier's Scopus, and Crarivate's Web of Science. We defined inclusion, exclusion, and quality of assessment criteria, and after a detailed review process, 66 publications were included in the final review. Our analysis revealed that the literature contains a significant amount of research to detect and analyze several attack types, such as the high yield investment programs and pump and dump. These attacks have been used to steal millions of USD, abuse millions of connected devices, and have created even more significant loss in denial of services and productivity losses. We have found that the researchers use various sources to collect training datasets. Many authors have made their dataset publicly available. We have created a list of these datasets, which we have made available along with other supplementary websites, tools, and libraries that can be used in the data collection and analysis process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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