Lightweight matrix-based authentication protocol for RFID
Bibliographic record
Abstract
Because of simplicity, low cost, and convenience of use, radio frequency identification (RFID) is gaining popularity in a wide variety of applications. At the same time, the threats that RFID systems are susceptible to are also evolving. The practical deployment of RFID systems depends highly on effective security mechanisms that can mitigate numerous intrusions. Providing security in the RFID system is challenging because of its limited power and computational capabilities. Mitigating denial-of-service attacks and providing bi-directional authentication to the tag and reader are the two of the most challenging security issues in RFID systems. In this regard, Karthikeyan and Nesterenko have proposed a matrix-based security protocol that uses simple matrix-based operations and is lightweight in nature. However, their protocol is proven to be vulnerable to de-synchronization and replay attacks. In this paper, we propose a modified matrix-based security protocol that can prevent de-synchronization and replay attacks yet providing a lightweight and effective security mechanism. The proposed protocol can be implemented on the hardware. A practical deployment of the proposed protocol is also feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".