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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
Claim

МЕТОДИЧЕСКИЕ ПОДХОДЫ К ПОСТРОЕНИЮ ЭФФЕКТИВНЫХ СИСТЕМ ОБНАРУЖЕНИЯ СЕТЕВЫХ АНОМАЛИЙ

descriptionPublicationkeyboard_double_arrow_right Article 08 Oct 2025 Russian Publisher:Zenodo

doi: 10.5281/zenodo.17295642

МЕТОДИЧЕСКИЕ ПОДХОДЫ К ПОСТРОЕНИЮ ЭФФЕКТИВНЫХ СИСТЕМ ОБНАРУЖЕНИЯ СЕТЕВЫХ АНОМАЛИЙ

Abstract

В работе представлен методологический подход к классификации аномалий сетевого трафика и описана взаимосвязь их источников, областей проявления и характерных признаков. Рассмотрены оптимизированные схемы детекции аномалий и злоупотреблений на основе пороговых, статистических и машинно-обученных методов. Предложена формальная модель для выявления и классификации аномальных событий в распределённых сетях, обеспечивающая высокую точность обнаружения и адаптивную настройку параметров

Keywords

детекция, пороговые методы, классификация, адаптивная модель, машинное обучение, сетевая аномалия

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    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green