Algorithm for improving interpretability of support vector models for anomaly detection in network traffic
This paper is devoted to enhancing the development of an algorithm aimed at improving the interpretability of machine learning models used for detecting anomalies in network traffic, which is critical for modern cybersecurity systems. The focus is on one-class support vector machine (SVM) models, wh...
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| Published in: | Проблеми керування та інформатики |
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| Date: | 2025 |
| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
Інститут кібернетики ім. В.М. Глушкова НАН України
2025
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| Subjects: | |
| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/211402 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Algorithm for improving interpretability of support vector models for anomaly detection in network traffic / K. Kerimov,S. Kurbanov, Z. Azizova // Проблемы управления и информатики. — 2025. — № 3. — С. 66-73. — Бібліогр.: 5 назв. — англ. |