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Record W1955581029 · doi:10.1002/cpe.1827

Dual cryptography authentication protocol and its security analysis for radio frequency identification systems

2011· article· en· W1955581029 on OpenAlexaff
Huansheng Ning, Hong Liu, Laurence T. Yang, Yan Zhang

Bibliographic record

VenueConcurrency and Computation Practice and Experience · 2011
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsComputer scienceAuthentication protocolRadio-frequency identificationComputer networkReplay attackAuthentication (law)Hash functionEncryptionCryptographyHash-based message authentication codeCorrectnessCryptographic protocolSession keyComputer securityOtway–Rees protocolChallenge-Handshake Authentication ProtocolMessage authentication code

Abstract

fetched live from OpenAlex

SUMMARY The open radio frequency identification (RFID) air interface may suffer from severe threats that make security problem become a critical issue for RFID systems and applications. This paper proposes a dual cryptography authentication protocol (DCAP) for RFID systems. DCAP partitions randomly the tag identifier into two partial identifiers that are used in the forward link and in the backward link, respectively. The protocol applies hash function and shared‐key encryption algorithm to safeguard both forward and backward links and provides a three‐round authentication mode on each tag and reader in a session. Then, authentication is carried out by the primary, secondary, and final verifications. For a formal analysis, a graphical method Colored Petri Nets is applied to model and analyze the correctness of DCAP. We prove that the protocol owns tag anonymity and forward security and has the capability to resist major attacks such as replay, reader forgery, and tag forgery. Finally, the performance in terms of storage, communication overhead, and computation load is evaluated to demonstrate that the protocol has modest complexity and high efficiency. Copyright © 2011 John Wiley & Sons, Ltd.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.320
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations3
Published2011
Admission routes1
Has abstractyes

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