A Secure Routing Protocol in Proactive Security Approach for Mobile Ad-Hoc Networks
Bibliographic record
Abstract
Secure routing of Mobile Ad-hoc Networks (MANETs) is still a hard problem after years of research. We therefore propose to design a secure routing protocol in a new approach. This protocol starts from a prerequisite secure status and fortifies this status by protecting packets using identity-based cryptography and updating cryptographic keys using threshold cryptography periodically or when necessary. Compared to existing schemes, the main contribution of our proposal is the notion of allowing only legitimate nodes to participate in the bootstrapping process, rather than trying to detect adversary nodes after they are participating in the routing protocol. Besides, the proposal has several improvements in routing setup and maintenance: it does not need any side channel or secret channel; it simplifies secret updates without requiring a node to move around; it does not use flooding to set up initial routing, and does not use multicast to update secrets.
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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 | 0.001 | 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 | 0.001 |
| 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".