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Record W2494130542 · doi:10.3233/jifs-16128

Fuzzy reliability analysis of integrated network traffic visualization system

2016· article· en· W2494130542 on OpenAlex
Amit Kumar Bhardwaj, Yuvraj Gajpal, Maninder Singh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Intelligent & Fuzzy Systems · 2016
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFault tree analysisReliability (semiconductor)Computer scienceVisualizationFuzzy logicReliability engineeringData miningTraffic analysisFault (geology)Real-time computingComputer networkArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Integrated Network Traffic Visualization System (INTVS) is a security system used for decision making against the malicious traffic. This paper presents usage of fuzzy fault tree analysis (FFTA) technique to get the reliability of INTVS. Fault-tree analysis incorporates the trapezoidal fuzzy numbers and minimal cut sets (MCSS) approach for reliability assessment of INTVS. The analysis performed in this paper can be used to calculate reliability of network security system. A case study is demonstrated to calculate the reliability of INTVS security system, which is implemented in Thapar University, India.

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.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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