A hybrid unsupervised clustering-based anomaly detection method

Pu, G; Wang, L; Shen, J; Dong, F

A hybrid unsupervised clustering-based anomaly detection method

Pu, G Wang, L Shen, J Dong, F

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Publisher:: Tsinghua University Press
Publication Type:: Journal Article
Citation:: Tsinghua Science and Technology, 2021, 26, (2), pp. 146-153
Issue Date:: 2021-04-01

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Field	Value	Language
dc.contributor.author	Pu, G
dc.contributor.author	Wang, L
dc.contributor.author	Shen, J
dc.contributor.author	Dong, F
dc.date.accessioned	2022-02-23T03:19:56Z
dc.date.available	2022-02-23T03:19:56Z
dc.date.issued	2021-04-01
dc.identifier.citation	Tsinghua Science and Technology, 2021, 26, (2), pp. 146-153
dc.identifier.issn	1007-0214
dc.identifier.issn	1878-7606
dc.identifier.uri	http://hdl.handle.net/10453/154799
dc.description.abstract	In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective and intelligent solutions are necessary. Unsupervised machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day attacks. In the current paper, we present an unsupervised anomaly detection method, which combines Sub-Space Clustering (SSC) and One Class Support Vector Machine (OCSVM) to detect attacks without any prior knowledge. The proposed approach is evaluated using the well-known NSL-KDD dataset. The experimental results demonstrate that our method performs better than some of the existing techniques.
dc.language	en
dc.publisher	Tsinghua University Press
dc.relation.ispartof	Tsinghua Science and Technology
dc.relation.isbasedon	10.26599/TST.2019.9010051
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0912 Materials Engineering, 0913 Mechanical Engineering
dc.subject.classification	General Science & Technology
dc.title	A hybrid unsupervised clustering-based anomaly detection method
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0912 Materials Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2022-02-23T03:19:55Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	2

Abstract:

In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective and intelligent solutions are necessary. Unsupervised machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day attacks. In the current paper, we present an unsupervised anomaly detection method, which combines Sub-Space Clustering (SSC) and One Class Support Vector Machine (OCSVM) to detect attacks without any prior knowledge. The proposed approach is evaluated using the well-known NSL-KDD dataset. The experimental results demonstrate that our method performs better than some of the existing techniques.

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http://hdl.handle.net/10453/154799