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Record W2146070963

Improving ZRP Routing Protocol Against Black Hole Attacks In Mobile Adhoc Networks(MANET)

2014· article· en· W2146070963 on OpenAlexvenueno aff
Ali Gheibi Dehnashi, Iman Attarzadeh, Alireza Osareh

Bibliographic record

VenueJournal of academic and applied studies · 2014
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsnot available
Fundersnot available
KeywordsComputer networkComputer scienceZone Routing ProtocolWireless Routing ProtocolRouting protocolLink-state routing protocolEnhanced Interior Gateway Routing ProtocolDynamic Source RoutingInterior gateway protocolPacket drop attackOptimized Link State Routing ProtocolHybrid routingNetwork packetDistributed computing
DOInot available

Abstract

fetched live from OpenAlex

Mobile adhoc network (MANET) is the network in which mobile devices are connected by wireless media and there is no centralized management and fixed network infrastructure. Generally, routing protocols in adhoc networks are classified into proactive, reactive and hybrid. One of the important challenges in hybrid routing properties is the security of these protocols. As ZRP hybrid routing protocol has not appropriate security features against black hole attacks, in this paper, we propose a security mechanism based on estimated latency of the route reply packets reaching the source node and selecting the shortest secure route and it is protected against black hole attacks and finally a new hybrid routing protocol is presented. After simulating this protocol by simulation toolNS2, The simulation result shows that the proposed protocol showed less failure compared to standard ZRP protocol in the presence of malicious nodes of black hole in terms of some parameters as quality of service (QOS) such as the packets loss, packet delivery ratio, end to end delay.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.017
GPT teacher head0.291
Teacher spread0.274 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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