A2N-SFS: an advanced statistical adaptive feature selection method for enhancing intrusion detection systems in IoT systems.
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| Title: | A2N-SFS: an advanced statistical adaptive feature selection method for enhancing intrusion detection systems in IoT systems. |
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| Authors: | Walling, Supongmen1, supongmen@gmail.com, Lodh, Sibesh1, sibeshnitnagaland@gmail.com |
| Source: | Knowledge & Information Systems; Nov2025, Vol. 67 Issue 11, p10491-10543, 53p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 189212868 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A2N-SFS: an advanced statistical adaptive feature selection method for enhancing intrusion detection systems in IoT systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Walling%2C+Supongmen%22">Walling, Supongmen</searchLink><relatesTo>1</relatesTo>, <i>supongmen@gmail.com</i><br /><searchLink fieldCode="AU" term="%22Lodh%2C+Sibesh%22">Lodh, Sibesh</searchLink><relatesTo>1</relatesTo>, <i>sibeshnitnagaland@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>; Nov2025, Vol. 67 Issue 11, p10491-10543, 53p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189212868 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10115-025-02540-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 53 StartPage: 10491 Titles: – TitleFull: A2N-SFS: an advanced statistical adaptive feature selection method for enhancing intrusion detection systems in IoT systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Walling, Supongmen – PersonEntity: Name: NameFull: Lodh, Sibesh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02191377 Numbering: – Type: volume Value: 67 – Type: issue Value: 11 Titles: – TitleFull: Knowledge & Information Systems Type: main |
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