Secure and Efficient Smart-Card-Based Remote User Authentication Scheme for Multiserver Environment
Bibliographic record
Abstract
The growth of the Internet and telecommunication technology has facilitated remote access. During the last decade, many secure dynamic identity (ID)-based remote user authentication schemes have been proposed for the multiserver environment using smart cards. Recently, Li et al. point that the Lee et al. scheme is vulnerable to forgery attack, server spoofing attack, improper authentication, and unfriendly and inefficient password change. To overcome these security weaknesses, Li et al. propose a novel smart-card- and dynamic ID-based remote user authentication scheme for multiserver environments. In this paper, we show that the Li et al. scheme is also vulnerable to offline password guessing attack, stolen smart-card attack, forgery attack, and poor reparability. Their scheme does not also provide two-factor security. To provide a secure remote user authentication scheme for the multiserver environment and to overcome the security weaknesses, we propose an enhanced scheme. Our scheme is aimed at logically securing the data stored in the smart card and improving the dynamic property of the ID using password randomization for each session. Our scheme resists forgery attack, replay attack, stolen smart-card attack, offline password guessing attack, and spoofing attack. Our scheme's efficiency has been established analytically and confirmed through simulation.
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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".