tuzsutТруды учебных заведений связиProceedings of Telecommunication Universities1813-324X2712-8830СПбГУТ10.31854/1813-324X-2023-9-6-42-57tuzsut-528Research ArticleЭЛЕКТРОНИКА, ФОТОНИКА, ПРИБОРОСТРОЕНИЕ И СВЯЗЬELECTRONICS, PHOTONICS, INSTRUMENTATION AND COMMUNICATIONSАнализ методов идентификации трафика для управления ресурсами в SDNAnalyzing Traffic Identification Methods for Resource Management in SDNhttps://orcid.org/0000-0002-7736-7121ДмитриеваЮ. С.DmitrievaJ.

ассистент кафедры инфокоммуникационных сетей и систем Санкт-Петербургского государственного университета телекоммуникаций им. проф. М.А. Бонч-Бруевича

dmitrieva@sut.ruhttps://orcid.org/0009-0005-4241-8784ОкуневаД. В.OkunevaD.

кандидат технических наук, декан факультета инфокоммуникационных сетей и систем Санкт-Петербургского государственного университета телекоммуникаций им. проф. М.А. Бонч-Бруевича

okuneva.dv@sut.ruhttps://orcid.org/0000-0003-4077-6869ЕлагинВ. С.ElaginV.

кандидат технических наук, доцент, доцент кафедры инфокоммуникационных систем Санкт-Петербургского государственного университета телекоммуникаций им. проф. М.А. Бонч-Бруевича

v.elagin@sut.ruСанкт-Петербургский государственный университет телекоммуникаций им. проф. М.А. Бонч-БруевичаРоссияThe Bonch-Bruevich Saint-Petersburg State University of TelecommunicationsRussian Federation202325122023964257Copyright © Дмитриева Ю.С., Окунева Д.В., Елагин В.С., 20232023Дмитриева Ю.С., Окунева Д.В., Елагин В.С.Dmitrieva J., Okuneva D., Elagin V.This work is licensed under a Creative Commons Attribution 4.0 License.https://tuzs.sut.ru/jour/article/view/528

Статья посвящена анализу методов классификации трафика в сети SDN. Выполнен обзор аналитических подходов идентификации трафика для выявления применяемых в них решений, а также оценки их применимости в сети SDN. Рассмотрены виды машинного обучения и выполнен анализ входных параметров. Методы интеллектуального анализа, освещенные в научных статьях, систематизированы по следующим критериям: параметры идентификации трафика, модель нейронной сети, точность идентификации. На основании анализа результатов обзора сделан вывод о возможности применения рассмотренных решений, а также о необходимости формирования схемы сети SDN с модулем элементов искусственного интеллекта для балансировки нагрузки.

The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is made about the possibility of applying the considered solutions, as well as the need to form a scheme of SDN network with a module of artificial intelligence elements for load balancing.

Software-Defined Networking (SDN)Программно-конфигурируемая сеть (ПКС)Deep packet inspection (DPI)Machine learning (ML)Deep learning (DL)Convolutional Neural Network (CNN)искусственный интеллект (ИИ)Software-Defined Networking (SDN)Deep packet inspection (DPI)Machine learning (ML)Deep learning (DL)Convolutional Neural Network (CNN)Artificial Intelligence (AI)Публикация подготовлена в рамках прикладных научных исследований СПбГУТ, регистрационный номер 123060900012-6 в ЕГИСУ НИОКТРThe scientific article was prepared in the framework of applied scientific research of SPBSUT, registration number 123060900012-6 in EGISU R&D.ReferencesДмитриева Ю.С. Сравнительный анализ методов управления сетевыми ресурсами в сетях SDN // Труды учебных заведений связи. 2022. Т. 8. № 1. С. 78‒83. DOI:10.31854/1813-324X-2022-8-1-73-83Dmitrieva J. Comparative Analysis of Network Resource Management Methods in SDN. Proceedings of Telecommun. Univ. 2022;8(1):73‒83. 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The authors declare that there are no conflicts of interest present.