McCLS: Certificateless Signature Scheme for Emergency Mobile Wireless Cyber-Physical Systems
Bibliographic record
Abstract
Mobile Ad Hoc Network is a self-configurable and self-organizing wireless network of mobile devices without fixed infrastructure support, which makes it a good candidate as underlying communication network for the Cyber-Physical Systems in emergency conditions such as earthquake, flood, and battlefields. In these scenarios, efficient communication schemes with security support are especially desired. Two cryptography approaches, the public key cryptography and the identitybased cryptography, face the costly and complex key management problem and the “key escrow" problem in the real-life deployment. Recently, the certificateless public key cryptography (CL-PKC) was introduced to address these problems in previous approaches. However, the efficiency of the schemes based on CL-PKC is not high and can be improved further. In this paper, we present an improved certificateless signature scheme (McCLS) based on bilinear pairings. First, we theoretically compare the efficiency of McCLS with that of existing certificateless signature schemes (CLS). Second, an empirical study is conducted to compare the traditional AODV with the McCLS scheme based on AODV (McDV) in their efficiency and effectiveness against two most common attacks (i.e. redirection attack and rushing attack). Results from theoretical analysis show that the new McCLS scheme is more efficient than existing CLS solutions, and results from empirical studies show that the McDV is able to resist the two common attacks without causing substantial degradation of the network 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".