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Record W2033029648 · doi:10.1109/mnet.2012.6375888

Alertwheel: radial bipartite graph visualization applied to intrusion detection system alerts

2012· article· en· W2033029648 on OpenAlex

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

VenueIEEE Network · 2012
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceVisualizationIntrusion detection systemWorkflowParsingEnhanced Data Rates for GSM EvolutionData visualizationBipartite graphGraphData miningNetwork securityInformation visualizationTheoretical computer scienceArtificial intelligenceComputer networkDatabase

Abstract

fetched live from OpenAlex

Intrusion detection systems, or IDSs, are network security tools that generate huge quantities of information which are challenging to analyze. Information visualization is essential for efficiently parsing these data to discover the underlying causes of computer security breaches. AlertWheel is a user interface featuring a novel radial overview visualization, as well as filtering, drilling down, and saving and annotating subsets of data, to support the workflow of real network defense analysts. In designing AlertWheel, we identified new ways of displaying bipartite graphs (i.e., network diagrams showing links between two sets of nodes). The links in AlertWheel's visualizations are positioned and shaped to avoid occlusion of data, and three different edge bundling techniques are used to reduce clutter.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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