SPoIM: A close look at pollution attacks in P2P live streaming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peer-to-Peer (P2P) live streaming traffic has been growing at a phenomenal rate over the past few years. When the original streaming content is mixed with bogus data, the corresponding P2P streaming network is being subjected to a “pollution attack.” As the content is shared by peers, the bogus data can be spread widely in minutes. In this paper, we study the impact of a pollution attack in popular streaming models, under various network settings and configurations. The study was conducted in SPoIM, our emulation of real-world P2P streaming systems under pollution attacks, through which we observed that the feasibility of the attack is sensitive to the speed at which an attacker can modify content. Our experimental results showed that different streaming approaches are more vulnerable in one network configuration than the others, and that the impact and effectiveness of the attack is not dependent on the network size, but does highly depend on the network stability and the bandwidth availability of the polluters and the source. Based the experimental results, we suggested possible improvements in streaming models to defend themselves against the pollution attack. Finally, we examined possible defense mechanisms and demonstrated the effectiveness of a reputation-based defense mechanism against a typical pollution attack.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it