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Deep learning enabled class imbalance with sand piper optimization based intrusion detection for secure cyber physical systems

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Abstract

A cyber physical system (CPS) is a network of cyber (computation, communication) and physical (sensors, actuators) components which interact with one another in a feedback form with human intervention. CPS authorizes the critical infrastructure and is treated as essential in day to day life as they exist in the basis of future smart devices. The increased exploitation of the CPS results in various threats and becomes a global problem. Therefore, it becomes essential to develop a safe, efficient, and robust CPS for real tine environment. For resolving this problem and accomplish security in CPS environment, intrusion detection system (IDS) can be developed. This study introduces an imbalanced generative adversarial network (IGAN) with optimal kernel extreme learning machine (OKELM), called IGAN-OKELM technique for intrusion detection in CPS environment. The proposed IGAN-OKELM technique mainly aims to address the class imbalance problem and intrusion detection. Besides, the IGAN-OKELM technique involves the IGAN model handling the class imbalance problem by the use of imbalanced data filter and convolution layers to the conventional generative adversarial network (GAN), which generates new instances for minority class labels. Moreover, the OKELM model is applied as a classifier and the optimal parameter tuning of the KELM model is performed by the use of sand piper optimization (SPO) algorithm and thereby improvises the intrusion detection performance. A wide ranging simulation analysis is carried out using benchmark dataset and the results are examined under varying aspects. The experimental results reported the better performance of the IGAN-OKELM technique over the recent state of art approaches interms of different measures.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (46/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R136), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia

    Anwer Mustafa Hilal, Abdelwahed Motwakel, Mohammed Rizwanullah & Ishfaq Yaseen

  2. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia

    Shaha Al-Otaibi & Ghadah Aldehim

  3. Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha, Saudi Arabia

    Hany Mahgoub & Fahd N. Al-Wesabi

  4. Department of Information Systems, College of Computer and Information Technology, Sana’a University, Sana’a, Yemen

    Fahd N. Al-Wesabi

Authors
  1. Anwer Mustafa Hilal
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  2. Shaha Al-Otaibi
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  3. Hany Mahgoub
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  4. Fahd N. Al-Wesabi
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  6. Abdelwahed Motwakel
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  7. Mohammed Rizwanullah
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  8. Ishfaq Yaseen
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Contributions

SA and HM—Conceptualization. HM and FNA—Data curation and Formal analysis. FNA and GA—Investigation and Methodology. GA and AM—Project administration and Resources. AM, MR, IY—Validation and Visualization. AMH—Writing—original draft, AMH, IY and MR—Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Anwer Mustafa Hilal.

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The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Hilal, A.M., Al-Otaibi, S., Mahgoub, H. et al. Deep learning enabled class imbalance with sand piper optimization based intrusion detection for secure cyber physical systems. Cluster Comput 26, 2085–2098 (2023). https://doi.org/10.1007/s10586-022-03628-w

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  • DOI: https://doi.org/10.1007/s10586-022-03628-w

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