Аналитический обзор подходов к обнаружению вторжений, основанных на федеративном обучении: преимущества использования и открытые задачи
Ключевые слова:
обнаружение вторжений, аномалии, федеративное обучение, модели анализа, разделение данныхАннотация
Для обеспечения точного и своевременного реагирования на различные типы атак системы обнаружения вторжений собирают и анализируют большое количество данных, которые могут включать в том числе и информацию с ограниченным доступом, например, персональные данные или данные, представляющие коммерческую тайну. Следовательно, такие системы могут быть рассмотрены как источник рисков, связанных с обработкой конфиденциальной информации и нарушением ее безопасности. Применение парадигмы федеративного обучения для построения аналитических моделей обнаружения атак и аномалий может значительно снизить такие риски, поскольку данные, генерируемые локально, не передаются какой-либо третьей стороне, а обучение модели осуществляется локально – на источниках данных. Использование федеративного обучения для обнаружения вторжений позволяет решить проблему обучения на данных, которые принадлежат различным организациям, и которые в силу необходимости обеспечения защиты коммерческой или другой тайны, не могут быть выложены в открытый доступ. Таким образом, данный подход позволяет также расширить и разнообразить множество данных, на которых обучаются аналитические модели анализа и повысить тем самым уровень детектируемости разнородных атак. Благодаря тому, что этот подход способен преодолеть вышеупомянутые проблемы, он активно используется для проектирования новых подходов к обнаружению вторжений и аномалий. Авторы систематизировано исследуют существующие решения для обнаружения вторжений и аномалий на основе федеративного обучения, изучают их преимущества, а также формулируют открытые проблемы, связанные с его применением на практике. Особое внимание уделяется архитектуре предлагаемых систем, применяемым методам и моделям обнаружения вторжений, а также обсуждаются подходы к моделированию взаимодействия между множеством пользователей системы и распределению данных между ними. В заключении авторы формулируют открытые задачи, требующие решения для применения систем обнаружения вторжений, основанных на федеративном обучении, на практике.
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Опубликован
2023-09-25
Как цитировать
Новикова, Е. С., Федорченко, Е. В., Котенко, И. В., & Холод, И. И. (2023). Аналитический обзор подходов к обнаружению вторжений, основанных на федеративном обучении: преимущества использования и открытые задачи. Информатика и автоматизация, 22(5), 1034-1082. https://doi.org/10.15622/ia.22.5.4
Раздел
Информационная безопасность
Copyright (c) Евгения Сергеевна Новикова, Елена Владимировна Федорченко, Игорь Витальевич Котенко, Иван Иванович Холод
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