Secure Data Transfer in Manet with Key Calculator and Key Distributer Using Cryptography Methods
Bibliographic record
Abstract
A Mobile Ad Hoc Network (MANET) is combined with number of versatile nodes that can communicate with one another without having any predefined foundation. These versatile nodes in the MANET go about as routers to transfer the information from source to destination. Since there is an expansion in number of portable clients and its applications, the versatile nodes security assumes a significant job in it. Even there are many methods for providing security to MANET, there are still several attacks causing in MANET. Secure data transfer in MANET can be achieved by introducing strong cryptographic methods and key exchange techniques. The reason for key generation and key maintenance is to give secure techniques for avoiding malicious activities in the MANET and to increase system performance. In this paper a strong cryptographic method is proposed, which generates and maintains keys and distribute keys safely to trusted nodes avoiding malicious nodes. The proposed method detects the malicious nodes and avoids them to participate in communication to improve packet delivery rate and to reduce delay in the network. The proposed method considers a node as a MANET Key Calculator (MKC) which generates keys and selects another node as MANET Key Distributer (MKD) for providing secure data transfer in MANET by applying cryptography methods. The proposed method is compared with traditional methods and the results show that the proposed method is exhibiting better performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".