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Record W2163876445 · doi:10.1109/glocom.2008.ecp.353

A Secure Key Management Scheme for Wireless and Mobile Ad Hoc Networks Using Frequency-Based Approach: Proof and Correctness

2008· article· en· W2163876445 on OpenAlexaff
Azzedine Boukerche, Yonglin Ren, Samer Samarah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsKey managementComputer scienceKey (lock)Key distributionEncryptionCorrectnessComputer securityComputer networkWireless ad hoc networkMobile ad hoc networkScheme (mathematics)Security associationPublic-key cryptographySymmetric-key algorithmKey generationWirelessTelecommunicationsCloud computingAlgorithmSecurity information and event managementCloud computing security

Abstract

fetched live from OpenAlex

Security plays an important role in today's information technology, particularly in wireless and mobile environments due to the lack of pre-deployed infrastructure and the unsuitability of centralized management. Since the encryption technique has been introduced to provide secure communications, it is critical to manage all kinds of keys efficiently when the network size is large or the topology undergoes frequent changes. This paper presents a novel key management scheme that not only employs both symmetric and asymmetric key algorithms, but also achieves its key updates through a frequency-based approach. In addition, our scheme provides necessary protection for other aspects of key management, such as data confidentiality, key distribution, etc. Through the discussion of the current issues of key management and a further analysis of our key management scheme, our solution is proven to be secure in the process of key management and to be robust against attacks aimed at causing malicious key updates.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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