A system analysis of reputation-base defences against pollution attacks in P2P streaming
Bibliographic record
Abstract
In recent years, the demand for multimedia streaming is soaring over the Internet. Due to the lack of a centralized administrative point, Peer-to-Peer (P2P) streaming system is vulnerable to pollution attacks, in which video segments might be altered by any peer before being shared. Among existing proposals, reputation-based defence mechanisms are the most effective and practical solutions. In this paper, we perform a measurement study on the effectiveness of this class of solutions. We implement a framework that allows us to simulate different variations of the reputation rating systems, from the global approach to the decentralized local approach, under different parameter settings and pollution models. In order to ensure the framework and the simulated solution is representative enough, we dissect existing proposals and implement a flexible defence mechanism, in which different components may be enabled and disabled by simply tuning certain parameters. Our results reveal that global knowledge of the content flow in the network does not necessarily improve the performance. It is often susceptible under collaborative attacks. We also find that expelling misbehaving peers is often more useful to prevent attacks than limiting their likelihood to be connected, although this can lead to poor playback quality.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".