SReD: A Secure REputation-based Dynamic Window Scheme for disruption-tolerant networks
Bibliographic record
Abstract
Disruption-tolerant networks (DTNs) provide a promising low-cost solution to transfer data in network environment where the connectivity is sporadic and unpredictable. Many existing methods for opportunistic data forwarding depend on the hypothesis that every node forwards messages regardless of the identities of the senders or receivers, however, the networks based on such methods are fragile under baleful attacks, such as black hole, denial of service (DoS), and wormhole. In this paper, we present a security strategy, namely SReD, to mitigate a number of known routing layer attacks. Our solution is a localized, link-state-based and multi-path routing protocol. We employ dynamic window mechanism to switch between reputation-based routing generation mode and probabilistic routing generation mode, and the proposed SReD is particularly suitable for resource-constrained DTNs. The proposed scheme has been compared with Epidemic and Prophet protocols in terms of efficiency and effectiveness against three common attacks. The results show that SReD is robust to these attacks and is more efficient under different metrics.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".