Provisioning secure on-demand routing protocol in mobile ad hoc network
Bibliographic record
Abstract
Mobile ad-hoc network (MANET) has been a leading technology for ubiquitous networking since a decade, in which an ad-hoc routing is one of its fundamental components. Due to a number of inevitable challenges in MANETs, especially a problem of secure routing is long-standing, many researchers have extensively studied and developed various techniques to secure on-demand routing protocols in MANETs. However, many open issues remain in secure on-demand routing for MANETs. In this paper, we propose a lightweight and efficient security mechanism for on-demand source routing protocol including DSR in MANETs. The proposed scheme provides key generation by using a self-certified public keying technique as well as ensures secure route discovery by employing Schnorr digital signature and multi-signatures scheme. We provide security analysis of the proposed scheme. It can be seen that the proposed approach is more secure than the existing schemes. We also evaluated the proposed approach using computer simulation and compared its performance to that of SRP. The results show that the proposed mechanism is better than SRP.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".