Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/543510
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Network Security | |
dc.date.accessioned | 2024-02-02T05:49:40Z | - |
dc.date.available | 2024-02-02T05:49:40Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/543510 | - |
dc.format.extent | xvii, 149 | |
dc.language | English | |
dc.relation | 146 | |
dc.rights | university | |
dc.title | A study and the implementation of machine learning algorithms with feature reduction mechanism for intrusion detection system | |
dc.title.alternative | ||
dc.creator.researcher | Sah, Gulab | |
dc.subject.keyword | Deep Learning | |
dc.subject.keyword | Intrusion Detection System | |
dc.subject.keyword | Machine Learning | |
dc.description.note | ||
dc.contributor.guide | Banerjee, Subhasish | |
dc.publisher.place | Jote | |
dc.publisher.university | National Institute of Technology Arunachal Pradesh | |
dc.publisher.institution | Department of Computer Science and Engineering | |
dc.date.registered | 2019 | |
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | 30cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Department of Computer Science and Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 628.15 kB | Adobe PDF | View/Open |
02_prelim.pdf | 2.25 MB | Adobe PDF | View/Open | |
03_content.pdf | 238.12 kB | Adobe PDF | View/Open | |
04 abstract.pdf | 128.6 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 351.12 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 552.91 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 147.38 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 681.19 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 955.75 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.34 MB | Adobe PDF | View/Open | |
11_annexures.pdf | 673.16 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 126.16 kB | Adobe PDF | View/Open |
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