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Double fuzzy clustering-driven context neural network for intrusion detection in cloud computing.

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Title: Double fuzzy clustering-driven context neural network for intrusion detection in cloud computing.
Authors: Anu Velavan, S.1 (AUTHOR) avelvan26@gmail.com, Sureshkumar, C.2 (AUTHOR)
Source: Wireless Networks (10220038). Mar2025, Vol. 31 Issue 3, p2513-2524. 12p.
Subjects: United States. Defense Advanced Research Projects Agency, Metaheuristic algorithms, Artificial intelligence, Invisible Web, Cloud computing, Blended learning, Intrusion detection systems (Computer security)
Abstract: Cyber security must be implemented when using cloud computing to identify and protect malevolent intrusions and strengthen the organizations capacity against cyberattacks. Detecting network intrusions with zero false alarms is a challenge. A number of intrusion detection systems (IDS) for cloud computing (CC) environments have put forward recently. The existing IDS exhibit significant false positive rates, poor classification accuracy, and over-fitting. Therefore, a Double Fuzzy Clustering-Driven Context Neural Network for Intrusion Detection in Cloud Computing (DFCCNN-BWOA-IDC) is proposed in this paper. Initially, the input data is gleaned from DARPA dataset. The input data is pre-processed utilizing Sequential pre-processing through orthogonalization (SPORT) method to replace the missing values and remove the duplicate values. After that, the pre-processing data is fed to the recursive feature elimination (REF) approach for selecting optimal features. Then the selected features are supplied to the DFCCNN to categorize the data as Normal or Anomaly. Finally, the Beluga Whale Optimization algorithm (BWOA) is proposed to enhance the weight parameters of DFCCNN classifier, which precisely detects the attacks. The proposed DFCCNN-BWOA-IDC approach is activated in MATLAB. The DFCCNN-BWOA-IDC method reaches better accuracy of 98.89% which is 15.98%, 13.59% and 19.53% higher than the existing approaches, like intrusion detection in CC with the help of hybrid deep learning approach (DKNN-CRDO-IDC), intrusion detection scheme under hybrid teacher learning optimization facilitates deep RNN in web and cloud computing (TL-DRNN-IDC), Intrusion detection scheme utilizing deep learning and Capuchin Search Algorithm for cloud and IoT (CNN-CapSA-IDC) respectively. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Networks (10220038) is the property of Springer Nature 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.)
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  Data: Cyber security must be implemented when using cloud computing to identify and protect malevolent intrusions and strengthen the organizations capacity against cyberattacks. Detecting network intrusions with zero false alarms is a challenge. A number of intrusion detection systems (IDS) for cloud computing (CC) environments have put forward recently. The existing IDS exhibit significant false positive rates, poor classification accuracy, and over-fitting. Therefore, a Double Fuzzy Clustering-Driven Context Neural Network for Intrusion Detection in Cloud Computing (DFCCNN-BWOA-IDC) is proposed in this paper. Initially, the input data is gleaned from DARPA dataset. The input data is pre-processed utilizing Sequential pre-processing through orthogonalization (SPORT) method to replace the missing values and remove the duplicate values. After that, the pre-processing data is fed to the recursive feature elimination (REF) approach for selecting optimal features. Then the selected features are supplied to the DFCCNN to categorize the data as Normal or Anomaly. Finally, the Beluga Whale Optimization algorithm (BWOA) is proposed to enhance the weight parameters of DFCCNN classifier, which precisely detects the attacks. The proposed DFCCNN-BWOA-IDC approach is activated in MATLAB. The DFCCNN-BWOA-IDC method reaches better accuracy of 98.89% which is 15.98%, 13.59% and 19.53% higher than the existing approaches, like intrusion detection in CC with the help of hybrid deep learning approach (DKNN-CRDO-IDC), intrusion detection scheme under hybrid teacher learning optimization facilitates deep RNN in web and cloud computing (TL-DRNN-IDC), Intrusion detection scheme utilizing deep learning and Capuchin Search Algorithm for cloud and IoT (CNN-CapSA-IDC) respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature 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.)
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        Value: 10.1007/s11276-024-03890-3
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        Text: English
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        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Invisible Web
        Type: general
      – SubjectFull: Cloud computing
        Type: general
      – SubjectFull: Blended learning
        Type: general
      – SubjectFull: Intrusion detection systems (Computer security)
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      – TitleFull: Double fuzzy clustering-driven context neural network for intrusion detection in cloud computing.
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            NameFull: Anu Velavan, S.
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              M: 03
              Text: Mar2025
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              Y: 2025
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