RFID Mutual Authentication Protocols based on Gene Mutation and Transfer
Bibliographic record
Abstract
Radio Frequency Identification (RFID) is a technology that is very popular due to the simplicity in its technology and high adaptability in a variety of areas. The simplicity in the technology, however, comes with a caveat – RFID tags have severe resource restrictions, which make them vulnerable to a range of security attacks. Such vulnerability often results in the loss of privacy of the tag owner and other attacks on tags. Previous research in RFID security has mainly focused on authenticating entities such as readers / servers, which communicate with the tag. Any security mechanism is only as strong as the encryption keys used. Since RFID communication is wireless, critical messages such as key exchange messages are vulnerable to attacks. Therefore, we present a mutual authentication protocol that relies on independent generation and dynamic updates of encryption keys thereby removing the need for key exchange, which is based on the concept of gene mutation and transfer. We also present an enhanced version of this protocol, which improves the security offered by the first protocol. The novelty of the proposed protocols is in the independent generation, dynamic and continuous updates of encryption keys and the use of the concept of gene mutation / transfer to offer mutual authentication of the communicating entities. The proposed protocols are validated by simulation studies and security analysis.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".