Uncovering Tor: An Examination of the Network Structure
Bibliographic record
Abstract
The dark web is a concealed portion of the Internet that can only be accessed through specialized software. Although multiple dark web technologies exist, with a common trait of using encryption to enforce anonymity, the Tor network remains the most prominent dark web network. To visit websites on the network, the user must use a heavily modified Firefox browser. The use of encryption to achieve anonymity poses a significant challenge for law enforcement that wishes to monitor users and content for illicit activity. This study examines Tor by focusing on the network structures created between websites via hyperlinks. Examining hyperlinks can provide insight into how virtual communities form on a network. We explore traditional social disorganization principles as a basis to draw comparisons between these virtual communities and real-life crime-prone neighborhoods. Automated data collection techniques were used to leverage the interconnected nature of domains on Tor. Using social network analysis, website hyperlinks are examined and core sites are identified. The analysis shows that these core sites form a significant portion of all connections made on the network with a density of 0.132. This core serves a critical function and has implications for detecting how users connect on Tor.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".