Bibliographic record
Abstract
As the capabilities of censors increase and their ability to perform more powerful deep-packet inspection techniques grows, more powerful systems are needed in turn to disguise user traffic and allow users under a censor's influence to access blocked content on the Internet. Decoy routing is a censorship resistance technique that hides traffic under the guise of a HTTPS connection to a benign, uncensored overt site. However, existing techniques far from perfectly mimic a typical access of content on the overt server. Artificial latency introduced by the system, as well as differences in packet sizes and timings betray their use to a censor capable of performing basic packet and latency analysis. While many of the more recent decoy routing systems focus on deployability concerns, they do so at the cost of security, adding vulnerabilities to both passive and active attacks. We propose Slitheen, a decoy routing system capable of perfectly mimicking the traffic patterns of overt sites. Our system is secure against previously undefended passive attacks, as well as known active attacks. Further, we show how recent innovations in traffic-shaping technology for ISPs mitigate previous deployability challenges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.020 |
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".