Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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1 Online-Ressource.
- Sprache
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Englisch
- Erschienen in
-
Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection ; volume:4 ; number:1 ; day:7 ; month:10 ; year:2024 ; pages:1-26 ; date:12.2024
Discover Internet of things ; 4, Heft 1 (7.10.2024), 1-26, 12.2024
- Urheber
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Walling, Supongmen
Lodh, Sibesh
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
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10.1007/s43926-024-00074-5
- URN
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urn:nbn:de:101:1-2412242111001.563952886617
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:32 MESZ
Datenpartner
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Beteiligte
- Walling, Supongmen
- Lodh, Sibesh
- SpringerLink (Online service)
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