Exploring the Factors Associated With Rejection From a Closed Cybercrime Community
Bibliographic record
Abstract
Research examining the illicit online market for cybercrime services operating via web forums, such as malicious software, personal information, and hacking tools, has greatly improved our understanding of the practices of buyers and sellers, and the social forces that structure actor behavior. The majority of these studies are based on open markets, which can be accessed by anyone with minimal barriers to entry. There are, however, closed communities operating online that are thought to operate with greater trust and reliability between participants, as they must be vetted and approved by existing community members. The decision to allow individuals to join a forum may reflect restrictive deterrence practices on the part of existing members, as those applicants may threaten the security or operations of the group. This study utilized a quantitative analysis to understand the factors associated with rejection for individuals who sought membership in the organized and sophisticated closed forum run by and for cybercriminals called Darkode. The findings demonstrated that individuals whose perceived engagement with the hacker community and cybercrime marketplace were considered too risky for membership. The implications of this study for our understanding of restrictive deterrence theory, as well as criminal market operations on and offline were explored in depth.
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.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".