Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets.
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| Title: | Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets. |
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| Authors: | Ullah, Saeed1 (AUTHOR), Wu, Junsheng1 (AUTHOR) wujunsheng@nwpu.edu.cn, Lin, Zhijun2 (AUTHOR), Kamal, Mian Muhammad3 (AUTHOR), Mostafa, Hala4 (AUTHOR), Sheraz, Muhammad5 (AUTHOR), Chuah, Teong Chee5 (AUTHOR) tcchuah@mmu.edu.my |
| Source: | Scientific Reports. 8/23/2025, Vol. 15 Issue 1, p1-31. 31p. |
| Database: | Academic Search Ultimate |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: asn DbLabel: Academic Search Ultimate An: 187497428 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-025-16553-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 1 Titles: – TitleFull: Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ullah, Saeed – PersonEntity: Name: NameFull: Wu, Junsheng – PersonEntity: Name: NameFull: Lin, Zhijun – PersonEntity: Name: NameFull: Kamal, Mian Muhammad – PersonEntity: Name: NameFull: Mostafa, Hala – PersonEntity: Name: NameFull: Sheraz, Muhammad – PersonEntity: Name: NameFull: Chuah, Teong Chee IsPartOfRelationships: – BibEntity: Dates: – D: 23 M: 08 Text: 8/23/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20452322 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Scientific Reports Type: main |
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