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Record W1995995106 · doi:10.1109/hoti.2013.17

Efficient Implementation of Security Applications in OpenFlow Controller with FleXam

2013· article· en· W1995995106 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpenFlowComputer scienceController (irrigation)Network packetOverhead (engineering)Computer networkFlexibility (engineering)Embedded systemRouting (electronic design automation)Distributed computingReal-time computingSoftware-defined networkingOperating system

Abstract

fetched live from OpenAlex

Current OpenFlow specifications provide limited access to packet-level information such as packet content, making it very inefficient, if not impossible, to deploy security and monitoring applications as controller applications. In this paper, we propose FleXam, a flexible sampling extension for OpenFlow designed to provide access to packet level information at the controller. Simplicity of FleXam makes it possible to implement it easily in OpenFlow switches and operate at line rate without requiring any additional memory. At the same time, its flexibility allows implementation of various monitoring and security applications in the controller, while maintaining balance between overhead and collected information details. FleXam realizes the advantages of both proactive and reactive routing schemes by providing a tunable trade-off between the visibility of individual flows, and the controller load. As an example, we demonstrate how FleXam can be used to implement a port scan detection application with an extremely low overhead.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.244
Teacher spread0.238 · 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

Quick stats

Citations56
Published2013
Admission routes1
Has abstractyes

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