ESLRAS: A Lightweight RFID Authentication Scheme with High Efficiency and Strong Security for Internet of Things
Bibliographic record
Abstract
Radio Frequency Identification (RFID) is one of the key technologies for Internet of Things (IoT). Due to the limitations of processing capability, storage space and power supply of RFID tag, the traditional security mechanisms cannot be used directly. In addition, the existing security threats become more severe towards RFID authentication scheme. In this paper, we propose an Efficient Secure Lightweight RFID Authentication Scheme based on challenge-response model, named ESLRAS. To achieve authentication efficiency, the key of the tag is chosen to reduce the number of hash computing in database. Furthermore, the key is stored in database and updated constantly with the tag to prevent the tracking attack and the synchronization attack. The correctness of ESLRAS has been proved using GNY logic, and the performance of ESLRAS in terms of security and efficiency is evaluated. Compared with other existing approaches, ESLRAS achieves stronger security and higher efficiency.
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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) | 0.001 | 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".