ESLRAS: A Lightweight RFID Authentication Scheme with High Efficiency and Strong Security for Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it