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Record W2550569116 · doi:10.1002/dac.3477

Cryptanalysis of an identity‐based authenticated key exchange protocol

2017· preprint· en· W2550569116 on OpenAlexaff
Younes Hatri, Ayoub Otmani, Kenza Guenda

Bibliographic record

VenueInternational Journal of Communication Systems · 2017
Typepreprint
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of Victoria
FundersAgence Nationale de la Recherche
KeywordsComputer securityComputer scienceAuthenticated Key ExchangeKey exchangeOakley protocolAdversarySession keyProtocol (science)Key (lock)CryptanalysisShared secretSecurity associationKey distributionCryptographyComputer networkPublic-key cryptographyEncryptionCloud computing securityNetwork Access Control

Abstract

fetched live from OpenAlex

Summary Authenticated key exchange protocols represent an important cryptographic mechanism that enables several parties to communicate securely over an open network. Elashry, Mu, and Susilo proposed an identity‐based authenticated key exchange (IBAKE) protocol where different parties establish secure communication by means of their public identities.The authors also introduced a new security notion for IBAKE protocols called resiliency, that is, if the secret shared key is compromised, the entities can generate another shared secret key without establishing a new session between them. They then claimed that their IBAKE protocol satisfies this security notion. We analyze the security of their protocol and prove that it has a major security flaw, which renders it insecure against an impersonation attack. We also disprove the resiliency property of their scheme by proposing an attack where an adversary can compute any shared secret key if just one secret bit is leaked.

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)0.002
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.056
GPT teacher head0.383
Teacher spread0.327 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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