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Evaluating the impact of generative adversarial models on the performance of anomaly intrusion detection.

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Title: Evaluating the impact of generative adversarial models on the performance of anomaly intrusion detection.
Authors: Arafah, Mohammad1,2, m.Arafah@lboro.ac.uk, Phillips, Iain1, Adnane, Asma1
Source: IET Networks (Wiley-Blackwell); Jan2024, Vol. 13 Issue 1, p28-44, 17p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 174818378
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22IET+Networks+%28Wiley-Blackwell%29%22">IET Networks (Wiley-Blackwell)</searchLink>; Jan2024, Vol. 13 Issue 1, p28-44, 17p
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1049/ntw2.12098
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 28
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      – TitleFull: Evaluating the impact of generative adversarial models on the performance of anomaly intrusion detection.
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            NameFull: Arafah, Mohammad
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            NameFull: Phillips, Iain
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            NameFull: Adnane, Asma
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            – D: 01
              M: 01
              Text: Jan2024
              Type: published
              Y: 2024
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              Value: 13
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