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Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks.

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Title: Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks.
Authors: Arafah, Mohammad1, Mohammad.Arafah@uop.edu.jo, Phillips, Iain2, i.w.phillips@lboro.ac.uk, Adnane, Asma2, a.adnane@lboro.ac.uk, Hadi, Wael1, whadi@uop.edu.jo, Alauthman, Mohammad1, mohammad.alauthman@uop.edu.jo, Al-Banna, Abedal-Kareem3, abanna@uop.edu.jo
Source: Applied Soft Computing; Jan2025, Vol. 168, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 181572334
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=181572334
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.asoc.2024.112455
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Titles:
      – TitleFull: Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks.
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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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            NameFull: Hadi, Wael
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            NameFull: Alauthman, Mohammad
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            NameFull: Al-Banna, Abedal-Kareem
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            – D: 01
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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              Value: 168
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