Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/123456789/32432
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dc.contributor.authorLapina, M. A.-
dc.contributor.authorЛапина, М. А.-
dc.date.accessioned2025-12-12T12:36:15Z-
dc.date.available2025-12-12T12:36:15Z-
dc.date.issued2026-
dc.identifier.citationSowmya, T., Mary Anita, E. A., Lapina, M. UK-IDS-Machine Learning Based Intrusion Detection System for Unknown Attack Detection // Lecture Notes in Networks and Systems. - 2026. - 1456 LNNS. - pp. 425 - 433. - DOI: 10.1007/978-3-032-07275-7_38ru
dc.identifier.urihttps://dspace.ncfu.ru/handle/123456789/32432-
dc.description.abstractComputer networks have become the major focus for attackers. Hence intrusion detection system plays a significant role in detecting attacks. Many researchers have already focused on the domain of cyber security by developing an efficient framework. However, developing an efficient IDS is still a challenging task because of its effectiveness in determining novel attacks. Hence in the current study, a machine learning based IDS called UK-IDS is proposed by incorporating OC-SVM and a basic SVM model. The aim of the proposed system is to achieve high accuracy and F1 score by detecting novel attacks. The OC-SVM approach identifies the novel attacks by collaborating the clustering and thresholding mechanism. The basic SVM model is to distinguish the type of attack. The experimental study reveals that UK-IDS framework shows good performance in terms of accuracy and F1 score.ru
dc.language.isoenru
dc.publisherSpringer Science and Business Media Deutschland GmbHru
dc.relation.ispartofseriesLecture Notes in Networks and Systems-
dc.subjectArtificial intelligence (AI)ru
dc.subjectIntrusion detection system (IDS)ru
dc.subjectOne class SVM (OC-SVM)ru
dc.subjectUnknown - intrusion detection system (UK-IDS)ru
dc.titleUK-IDS-Machine Learning Based Intrusion Detection System for Unknown Attack Detectionru
dc.typeСтатьяru
vkr.instФакультет математики и компьютерных наук имени профессора Н.И. Червяковаru
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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