Two‐factor mutual authentication with key agreement in wireless sensor networks
Bibliographic record
Abstract
Abstract Wireless sensor networks (WSNs) are getting popular for their deployment in unattended environments, where a registered user can log in to the network and access data collected from the desired sensor. Because of limited resources and computation power in sensor nodes, an authentication protocol should be simple and efficient. M.L. Das proposed a two‐factor authentication scheme for WSNs. Because his scheme uses only one‐way hash function and XOR operation, it is well suited for resource‐constrained environments. Because of some flaws in Das's scheme, several improved schemes have been introduced. In this paper, we show that Das's scheme and its derivatives not only have security imperfections but also do not provide key agreement. To overcome their security shortcomings, we propose a novel user authentication scheme with key agreement for WSN. We furnish security analysis of the proposed protocol to show its robustness to various attacks as well as analyze its performance to determine its efficiency. We provide protocol analysis and verification of the proposed protocol. Compared with the existing schemes, it is more robust and offers better security. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".