Designing P2P Networks Tolerant to Attacks and Faults Based on Bimodal Degree Distribution
Bibliographic record
Abstract
Abstract—Recently, in contrast with the centralized networks (e.g., traditional client/server systems), the distributed networks such as Peer-to-Peer (P2P) networks and grid networks have attracted much attention due to their scalability. While the distributed networks have the advantage of allowing the node(s) to join or leave the network easily, the issue of lack of resiliency to both attacks and faults still remains. In this paper, we classify the existing distributed networks based on their degree distributions. Then, we demonstrate that they are not resilient to attacks and/or faults. For example, unstructured P2P networks, which have a power-law degree distribution, are vulnerable to attacks such as DOS. To address and resolve this issue, we propose a method to construct a network following bimodal degree distribution, which is robust to deal with both attacks and faults. Performance evaluation is conducted through computer simulations, which show that the proposed method can achieve higher resilience compared with other existing networking approaches. Index Terms—P2P networks, overlay networks, attack and fault tolerance, degree distribution. I.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".