Обзор систем обнаружения сетевых вторжений, основанных на подходах глубокого обучения
https://doi.org/10.17586/2226-1494-2023-23-2-352-363
Аннотация
В настоящее время большинство ИТ-организаций отдают предпочтение среде облачных вычислений, которая имеет распределенный и масштабируемый характер. При этом гибкая и открытая архитектура среды облачных вычислений привлекает большое внимание потенциальных злоумышленников из-за киберугроз. В данном случае система обнаружения вторжений (Intrusion Detection System, IDS) играет важную роль в отслеживании вредоносных действий в облачных системах. В работе представлен системный обзор существующих IDS, основанных на различных методах, таких как интеллектуальный анализ данных, машинное обучение и методы глубокого обучения. В последнее время методы глубокого обучения широко распространены в области обнаружения вторжений при решении проблем конфиденциальности и угроз безопасности. В связи с этим важно исследовать подходы к исследованию глубокого обучения, применяемых на разных этапах процесса обнаружения вторжений. Выполнено сравнение подходов глубокого обучения и поверхностных методов машинного обучения. Приведено описание наборов данных, наиболее часто используемых в системах обнаружения вторжений.
Ключевые слова
Об авторах
Д. Аль-Сафар
Вавилонский университет
Ирак
Ирак
Аль-Сафар Дуа Вахаб Рахим — магистр, лектор
Кербала, 51002
В. Аль-Ясин
Технический институт Кербелы;Технический университет Аль-Фурат Аль-Авсат
Ирак
Ирак
Аль-Ясин Ватик Лафтах — доцент, доктор наук, руководитель компьютерного центра
Кербела, 56001
sc 57188754655
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Рецензия
Для цитирования:
Аль-Сафар Д., Аль-Ясин В. Обзор систем обнаружения сетевых вторжений, основанных на подходах глубокого обучения. Научно-технический вестник информационных технологий, механики и оптики. 2023;23(2):352-363. https://doi.org/10.17586/2226-1494-2023-23-2-352-363
For citation:
Al-Safaar D., Al-Yaseen W. A survey of network intrusion detection systems based on deep learning approaches. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2023;23(2):352-363. https://doi.org/10.17586/2226-1494-2023-23-2-352-363
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