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https://dspace.ncfu.ru/handle/123456789/32432| Title: | UK-IDS-Machine Learning Based Intrusion Detection System for Unknown Attack Detection |
| Authors: | Lapina, M. A. Лапина, М. А. |
| Keywords: | Artificial intelligence (AI);Intrusion detection system (IDS);One class SVM (OC-SVM);Unknown - intrusion detection system (UK-IDS) |
| Issue Date: | 2026 |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Citation: | Sowmya, 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_38 |
| Series/Report no.: | Lecture Notes in Networks and Systems |
| Abstract: | Computer 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. |
| URI: | https://dspace.ncfu.ru/handle/123456789/32432 |
| Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
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| File | Size | Format | |
|---|---|---|---|
| scopusresults 3828.pdf Restricted Access | 126.96 kB | Adobe PDF | View/Open |
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