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Record W2020209128 · doi:10.1057/ivs.2010.9

Accommodating IPv6 Addresses in Security Visualization Tools

2010· article· en· W2020209128 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

VenueInformation Visualization · 2010
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceIPv6IPv4VisualizationNetwork securityThe InternetComputer securityData scienceWorld Wide WebData mining

Abstract

fetched live from OpenAlex

Visualization is used by security analysts to help detect patterns and trends in large volumes of network traffic data. With IPv6 slowly being deployed around the world, network intruders are beginning to adapt their tools and techniques to work over IPv6 (versus IPv4). Many tools for visualizing network activity, while useful for detecting large-scale attacks and network behavior anomalies, still only support IPv4. In this article, we explore the current state of IPv6 support in some popular security visualization tools and identify the roadblocks preventing those tools from supporting the new protocol. We propose a filtering technique that helps reduce the occlusion of IPv6 sources on graphs and enables IPv4 visualization tools to display both IPv4 and IPv6 sources on a single graph. We also suggest using treemaps for visually representing the vast space of remote addresses in IPv6.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.013
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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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