Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data

dc.contributor.author	Verkerken, Miel
dc.contributor.author	D'hooge, Laurens
dc.contributor.author	Wauters, Tim
dc.contributor.author	Volckaert, Bruno
dc.contributor.author	De Turck, Filip
dc.contributor.imecauthor	Verkerken, Miel
dc.contributor.imecauthor	D'hooge, Laurens
dc.contributor.imecauthor	Wauters, Tim
dc.contributor.imecauthor	Volckaert, Bruno
dc.contributor.imecauthor	De Turck, Filip
dc.contributor.orcidimec	De Turck, Filip::0000-0003-4824-1199
dc.contributor.orcidimec	Wauters, Tim::0000-0003-2618-3311
dc.contributor.orcidimec	Volckaert, Bruno::0000-0003-0575-5894
dc.contributor.orcidimec	D'hooge, Laurens::0000-0001-5086-6361
dc.date.accessioned	2022-01-27T16:21:59Z
dc.date.available	2021-11-02T16:01:59Z
dc.date.available	2022-01-27T16:21:59Z
dc.date.issued	2020
dc.identifier.eisbn	978-0-7381-4292-0
dc.identifier.issn	na
dc.identifier.uri	https://imec-publications.be/handle/20.500.12860/37953
dc.publisher	IEEE
dc.source.conference	4th Cyber Security in Networking Conference (CSNet)
dc.source.conferencedate	OCT 21-23, 2020
dc.source.conferencelocation	Lausanne
dc.source.journal	na
dc.source.numberofpages	8
dc.subject.keywords	ANOMALY DETECTION
dc.title	Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data
dc.type	Proceedings paper
dspace.entity.type	Publication
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