Performance Evaluation of a Hybrid Cryptosystem with Authentication for Wireless Ad hoc Networks
Bibliographic record
Abstract
Though wireless networks provide great convenience to mobile users, they also give rise to non-trivial concerns about the security due to open channels and flexible mobility. An important concern is the securing of a communication link between any pair of nodes. Here, data encryption is deemed the primary solution to protect data confidentiality and integrity. However, the issue of traditional cryptographic techniques' application in wireless and mobile networks is significant to improve the performance of networks. In this article, we propose a hybrid encryption algorithm to deal with the applications of symmetric key and asymmetric key in wireless ad hoc networks. In particular, a public key is used as an authentication method to validate the identity of a legitimate node and to prevent unwanted parties from obtaining unauthorized data and resources. Our performance comparison shows that our proposed schemes have reasonable computational costs and communication overhead, provide reliable security, and at the same time, improve the efficiency of cryptographic techniques. In this way, our simulation results indicate that our algorithm is effective and practical for the data protection in wireless and mobile networks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".