View in EDS

An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization.

Saved in:
Bibliographic Details
Title: An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization.
Authors: Ali, Asad1 (AUTHOR), Assam, Muhammad2 (AUTHOR), Khan, Faheem Ullah2 (AUTHOR), Ghadi, Yazeed Yasin3 (AUTHOR), Nurdaulet, Zhumazhan4 (AUTHOR), Zhibek, Alibiyeva5 (AUTHOR), Shah, Syed Yaqub6 (AUTHOR), Alahmadi, Tahani Jaser1,7 (AUTHOR) tjalahmadi@pnu.edu.sa
Source: Computers & Electrical Engineering. Dec2024:Part C, Vol. 120, pN.PAG-N.PAG. 1p.
Subjects: Artificial neural networks, Multilayer perceptrons, Error rates, Security systems, Infrastructure (Economics), Intrusion detection systems (Computer security)
Abstract: The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Electrical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 181222700
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ali%2C+Asad%22">Ali, Asad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Assam%2C+Muhammad%22">Assam, Muhammad</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khan%2C+Faheem+Ullah%22">Khan, Faheem Ullah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ghadi%2C+Yazeed+Yasin%22">Ghadi, Yazeed Yasin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nurdaulet%2C+Zhumazhan%22">Nurdaulet, Zhumazhan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhibek%2C+Alibiyeva%22">Zhibek, Alibiyeva</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shah%2C+Syed+Yaqub%22">Shah, Syed Yaqub</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alahmadi%2C+Tahani+Jaser%22">Alahmadi, Tahani Jaser</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)<i> tjalahmadi@pnu.edu.sa</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Electrical+Engineering%22">Computers & Electrical Engineering</searchLink>. Dec2024:Part C, Vol. 120, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink><br /><searchLink fieldCode="DE" term="%22Security+systems%22">Security systems</searchLink><br /><searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Electrical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181222700
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.compeleceng.2024.109838
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Multilayer perceptrons
        Type: general
      – SubjectFull: Error rates
        Type: general
      – SubjectFull: Security systems
        Type: general
      – SubjectFull: Infrastructure (Economics)
        Type: general
      – SubjectFull: Intrusion detection systems (Computer security)
        Type: general
    Titles:
      – TitleFull: An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ali, Asad
      – PersonEntity:
          Name:
            NameFull: Assam, Muhammad
      – PersonEntity:
          Name:
            NameFull: Khan, Faheem Ullah
      – PersonEntity:
          Name:
            NameFull: Ghadi, Yazeed Yasin
      – PersonEntity:
          Name:
            NameFull: Nurdaulet, Zhumazhan
      – PersonEntity:
          Name:
            NameFull: Zhibek, Alibiyeva
      – PersonEntity:
          Name:
            NameFull: Shah, Syed Yaqub
      – PersonEntity:
          Name:
            NameFull: Alahmadi, Tahani Jaser
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 10
              M: 12
              Text: Dec2024:Part C
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 00457906
          Numbering:
            – Type: volume
              Value: 120
          Titles:
            – TitleFull: Computers & Electrical Engineering
              Type: main
ResultId 1