Модели и алгоритмы защиты систем обнаружения вторжений от атак на компоненты машинного обучения
(Стр. 17-25)

На сегодняшний день одним из средств защиты сетевой инфраструктуры от кибератак являются системы обнаружения вторжений. Цифровизация требует использования средств, которые позволяют справляться не только с известными видами атак, но и с ранее не описанными. Для защиты от таких угроз возможно использование машинного обучения. В работе представлены модели и алгоритмы защиты от атак уклонением на компоненты машинного обучения систем обнаружения вторжений. Новизна в том, что впервые было проведено моделирование применения подсистемы защиты на базе автоэнкодеров длительной-кратковременной памяти во время атаки быстрого градиентного знака. Методология заключается в моделирование состязательных атак с оценкой эффективности защиты классическими метриками: точность, полнота, F-мера. Результаты исследования показали эффективность предложенной подсистемы защиты компонентов машинного обучения систем обнаружения вторжений от атак уклонением. Показатели детектирования удалось восстановить практически до исходных значений.

ОБРАЗЕЦ ЦИТИРОВАНИЯ: Ичетовкин Е.А., Котенко И.В. Модели и алгоритмы защиты систем обнаружения вторжений от атак на компоненты машинного обучения // Computational Nanotechnology. 2025. Т. 12. № 1. С. 17-25. DOI: 10.33693/2313-223X-2025-12-1-17-25. EDN: LSJCNO

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кибербезопасность, системы обнаружения вторжений, компоненты машинного обучения, состязательные атаки, методы защиты.