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Record W1993027189 · doi:10.1109/icces.2007.4447017

Message security in mobile ad-hoc networks: Using trust-based multi-path routing approach

2007· article· en· W1993027189 on OpenAlexaff
Prayag Narula, Sanjay Kumar Dhurandher, Sudip Misra, Isaac Woungang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComputer networkMobile ad hoc networkWireless ad hoc networkRouting (electronic design automation)Optimized Link State Routing ProtocolPath (computing)Adaptive quality of service multi-hop routingAd hoc wireless distribution serviceRouting protocolDistributed computingComputer securityTelecommunicationsWireless

Abstract

fetched live from OpenAlex

Message security is of paramount importance in mobile ad-hoc networks (MANETs) due to several reasons including their applications in situations such as emergencies, crisis management, military and healthcare. However, because of the absence of a fixed infrastructure with a designated centralized access point, implementation of hard-cryptographic security is a challenging prospect. In this paper, we present a method of message security using trust-based multi-path routing. In this approach, less trusted nodes are given lower number of self-encrypted parts of a message, thereby making it difficult for malicious nodes to gain access to the minimum information required to break through the encryption strategy. Using trust levels, we make multi-path routing flexible enough to be usable in networks with ‘vital’ nodes and absence of necessary redundancy. In addition, using trust levels, we avoid non-trusted routes that may use of brute force attacks and may decrypt messages if enough parts of the message are available to them.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.265
Teacher spread0.245 · 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.

Study designSimulation or modeling
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

Citations4
Published2007
Admission routes1
Has abstractyes

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