LAS-SG: An Elliptic Curve-Based Lightweight Authentication Scheme for Smart Grid Environments
Bibliographic record
Abstract
The communication among smart meters (SMs) and neighborhood area network (NAN) gateways is a fundamental requisite for managing the energy consumption at the consumer site. The bidirectional communication among SMs and NANs over the insecure public channel is vulnerable to impersonation, SM traceability, and SM physical capturing attacks. Many existing schemes’ insecurities and/or inefficiencies call for an efficient and secure authentication scheme for smart grid infrastructure. In this article, we present a privacy preserving and lightweight authentication scheme for smart grid (LAS-SG) using elliptic curve cryptography. The proposedLAS-SGis proved as secure under the standard model. Moreover, the efficiency of the LAS-SG is extracted through a real-time experiment, which attests that proposedLAS-SGcompletes a round of authentication in 20.331 ms by exchanging only two messages and 192 B. Due to the adequate efficiency and ample security, the proposedLAS-SGis more appropriate for SG environments.
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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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".