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Record W2131221016 · doi:10.1109/icc.2012.6364233

A first look on the effects and mitigation of VoIP SPIT flooding in 4G mobile networks

2012· article· en· W2131221016 on OpenAlexaff
Elias Bou‐Harb, Mourad Debbabi, Chadi Assi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceVoice over IPComputer networkFlooding (psychology)Computer securityArchitectureNetwork packetNetwork architectureMobile telephonyNext-generation networkCellular networkMobile radioThe Internet

Abstract

fetched live from OpenAlex

The fourth generation of mobile networks is considered a technology-opportunistic and user-centric system. Part of its new architecture, 4G networks will implement an evolved packet core. Although this will provide various critical advantages, it will however expose telecom networks to serious IP-based attacks. One often adopted solution to mitigate such attacks is based on a centralized security architecture. This centralized approach nonetheless, requires large processing resources to handle large amount of traffic, which may result in a significant over dimensioning problem in the centralized nodes causing this approach to fail from achieving its security task. In this paper, we primarily contribute by presenting a first look on the DoS effects of VoIP SPIT flooding on 4G mobile networks. We further contribute by proposing a distributed architecture on the mobile network infrastructure that is secure, efficient and cost-effective.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2012
Admission routes1
Has abstractyes

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