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ZENODO
Article . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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СИСТЕМА ОБАНАРУЖЕНИЯ ВРЕДОНОСНОГО КОДА НА ИНТЕРПРЕТИРУЕМОМ ЯЗЫКЕ ПРОГРАММИРОВАНИЯ В ТРАФИКЕ СЕТИ

СИСТЕМА ОБАНАРУЖЕНИЯ ВРЕДОНОСНОГО КОДА НА ИНТЕРПРЕТИРУЕМОМ ЯЗЫКЕ ПРОГРАММИРОВАНИЯ В ТРАФИКЕ СЕТИ

Abstract

В статье исследуется проблема обнаружения файлов вредоносного кода интерпретируемого языка программирования Python. Обоснована актуальность создания системы обнаружения вредоносного кода в трафике сети и предложено программное решение по обнаружению вредоносных файлов, передаваемых в сетевом трафике. Представлена модель интеллектуальной системы обнаружения вредоносного кода в трафике сети, использующую подход «обработки естественного языка» для анализа текста файлов исходного кода Python. Показана схема работы разработанной системы, проведено сравнение готовой системы с аналогами, проверена эффективность используемой языковой модели машинного обучения, выполняющей классификацию кода.

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    popularity
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    influence
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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