Hijacking the Vuze BitTorrent network: all your hop are belong to us
Bibliographic record
Abstract
Vuze is a popular file‐sharing client. When looking for content, Vuze selects from its list of neighbours, a set of 20 nodes to be contacted; the selection is performed such that the neighbours closest to the content in terms of Vuze ID are contacted first. To improve efficiency of its searches, Vuze implements a network coordinate system: from the set of 20 to‐be‐contacted nodes, queries are sent to the closest nodes in terms of network distance, which is calculated by the difference in network coordinates. However, network coordinate systems are inherently insecure and a malicious peer can lie about its coordinate to appear closest to every peer in the network. This allows the malicious peer to bias next‐hop choices for victim peers such that queries will be sent to the attacker, thus hijacking every search query. In our experiments, almost 20% of the search queries are hijacked; the cost of performing this attack is minimal – less than $112/month.
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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 | 0.009 |
| 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 | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".