METHODS FOR DETECTING ANOMALIES IN NETWORK TRAFFIC BASED ON ONE-CLASS SVM TECHNOLOGY
Komil KerimovTashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Amir Temur Avenue 108, 100084, Tashkent city, Republic of Uzbekistan. E-mail: [email protected];С. А. КурбановTashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Amir Temur Avenue 108, 100084, Tashkent city, Republic of UzbekistanZarina AzizovaTashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Amir Temur Avenue 108, 100084, Tashkent city, Republic of Uzbekistan. E-mail: [email protected]
Himičeskaâ tehnologiâ. Kontrolʹ i upravlenie/Chemical Technology. Control and Managementjournal2025en
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ABI
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This article is dedicated to the research and application of the One-Class Support Vector Machines method for detecting anomalies in network traffic. It examines the problems of detecting anomalies in network traffic and proposes a methodology for using One-Class SVM, including an overview of the main concepts and formulas of the algorithm. A discussion of the results of One-Class SVM is presented, including interpretation, advantages, limitations and possible directions for development of the proposed technique, as well as the practical significance of using the proposed method for detecting anomalies in network traffic.
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