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Record W2590793943 · doi:10.1049/el.2016.2234

Effective software‐defined networking controller scheduling method to mitigate DDoS attacks

2017· article· en· W2590793943 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

VenueElectronics Letters · 2017
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsCarleton University
Fundersnot available
KeywordsDenial-of-service attackComputer scienceSoftware-defined networkingQueueScheduling (production processes)Computer networkScheduleSoftwareController (irrigation)Distributed computingReal-time computingOperating systemEngineeringThe Internet

Abstract

fetched live from OpenAlex

To improve the availability of the software‐defined networking (SDN) under distributed denial of service (DDoS) attacks, a multi‐queue SDN controller scheduling algorithm based on time slice allocation strategy is proposed. The proposed algorithm can take different time slice allocation strategies according to the intensity of DDoS attacks and use SDN controller to schedule processing flow request from different switches (including the switches suffering from different degrees of attacks and normal switches) to ensure better protection to the normal switch in the network under DDoS attacks. Simulation results are presented to show the effectiveness of the proposed algorithm.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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