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Record W3000500766 · doi:10.1109/tii.2020.2966708

<i>BloomStore</i>: Dynamic Bloom-Filter-based Secure Rule-Space Management Scheme in SDN

2020· article· en· W3000500766 on OpenAlexaff
Amritpal Singh, Shalini Batra, Gagangeet Singh Aujla, Neeraj Kumar, Laurence T. Yang

Bibliographic record

VenueIEEE Transactions on Industrial Informatics · 2020
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsBloom filterComputer scienceHash functionHash tableComputer networkDistributed computingSoftware-defined networkingController (irrigation)Distributed hash tableComputer security

Abstract

fetched live from OpenAlex

Software-defined networking (SDN) provides an efficient way of managing traffic load by shifting complex and rigid computing tasks to the centralized controller. It reduces the burden on the switches, task of which is to perform the routing based upon the rule-action pair. However, the flow table storage capacity of switches is limited. It may have to face performance bottlenecks, which, in turn, can cause serious security breaches and performance degradation. Hence, in this article, BloomStore, which is a dynamic bloom-filter-based secure rule-space management scheme in SDN, is proposed. BloomStore handles the data traffic dynamically by managing network resources. A twofold security check is used for secure data transfer using double hashing, i.e., two independent hash functions are used to generate k hash functions. Moreover, partitioned hashing is proposed to have insertion and query in a bucket of bloom array. The result analysis demonstrates that BloomStore outperforms its competing variants with respect to various performance parameters.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
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 head0.033
GPT teacher head0.232
Teacher spread0.198 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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