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Record W2039936570 · doi:10.1109/ccece.2013.6567741

Symmetric key based RFID authentication protocol with a secure key-updating scheme

2013· article· en· W2039936570 on OpenAlexafffund
Guangyu Zhu, Gul N. Khan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsComputer scienceMutual authenticationAuthentication protocolKey (lock)Computer securityAuthentication (law)EavesdroppingSymmetric-key algorithmComputer networkReplay attackSynchronization (alternating current)Block cipherProtocol (science)CryptographyCryptographic protocolPre-shared keyKey-agreement protocolChallenge–response authenticationSession keyKey exchangePublic-key cryptographyEncryptionKey distributionChannel (broadcasting)

Abstract

fetched live from OpenAlex

RFID technology is being widely employed in various pervasive applications. Therefore, security and privacy protection for RFID systems is an important issue that needs to be addressed. In this paper, we present a secure mutual authentication protocol for RFID systems that is based on symmetric key technique with an efficient key updating mechanism. The objective is to improve the RFID system security against replay, de-synchronization, eavesdropping and man-in-the-middle attacks while maintaining lower computation, communication and storage. We also compare our authentication method with three recent protocols that deploy the same block cipher (XTEA) by implementing all the protocols on the same RF based hardware. Our proposed authentication protocol provides high security level and low computation and communication costs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.006
GPT teacher head0.217
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations8
Published2013
Admission routes2
Has abstractyes

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