Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection.
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| Title: | Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection. |
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| Authors: | Zakariah, Mohammed1, mzakariah@ksu.edu.sa, AlQahtani, Salman A.2, salmanq@ksu.edu.sa, Al-Rakhami, Mabrook S.3, malrakhami@ksu.edu.sa |
| Source: | Applied Sciences (2076-3417); Jun2023, Vol. 13 Issue 11, p6504, 31p |
| 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: 164213785 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=164213785 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app13116504 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 6504 Titles: – TitleFull: Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zakariah, Mohammed – PersonEntity: Name: NameFull: AlQahtani, Salman A. – PersonEntity: Name: NameFull: Al-Rakhami, Mabrook S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 13 – Type: issue Value: 11 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
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