Putting all eggs in a single basket: A cross-community analysis of 12 hacking forums
Bibliographic record
Abstract
Hackers have established large online communities in the form of online discussion forums. These forums help hackers with their goals: to hack, commit credit card/identity fraud or theft, launder money, and carry out digital attacks on physical infrastructure. There is need from security organizations and law enforcement to monitor these activities to identify individuals of interest, emerging threats and malware, and develop effective disruption strategies. However, as studies have consistently shown that as these communities are disrupted, new ones emerge. This might lead users of these forums to not put all their eggs in a single forum but rather diversify their efforts across multiple forums, in which case the removal of a forum will not create a lot of damage to the overall larger community. This is where the literature falls short, there is a dearth of knowledge about how hackers behave across forums, whether they do diversify. This paper aims to add to this knowledge by studying hacker activity across 12 online discussion forums, and identifying users who might not be relevant in a single forum, but are key across the larger community. This would allow for the targeting of specific users and the disruption of the flow of information across the larger community.
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 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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".