Location, Location, Location: Mapping Potential Canadian Targets in Online Hacker Discussion Forums
Bibliographic record
Abstract
The goal of this paper was to analyze hacker forums to better understand the threats they pose to Canadian critical systems specifically and cyber-security more generally. To facilitate the data collection, a customized web-crawler was developed to specifically capture the structured content posted to forums. Three hacker forums were selected for analysis that represented different facets of the hacker community: carding (data theft), coding (malware development and deployment), and security (distribution of vulnerabilities). We identified and geolocated user disclosed IP addresses to try to identify critical systems and determine the extent as well as context in which critical systems were openly discussed by forum users. In total, 311,501 analyzable IP addresses were extracted from the data with 3,168 (1%) geolocated to Canada. The prevalence of Canadian IP addresses does not indicate their potential for exploitation, although it does highlight a perceived heightened interest in Canadian critical systems by hacker forum users. Potential at-risk systems included government agencies, universities across Canada, and private industries within the transportation network, namely aviation and shipping firms.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".