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Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure

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Abstract

In the present study, a novel application of backpropagated neurocomputing heuristics (BNCH) is presented for epidemic virus model that portrays the Stuxnet virus propagation in regimes of supervisory control and data acquisition (SCADA) networks using multi-layer structure of neural networks (NNs) optimized with competency of efficient backpropagation with Levenberg–Marquardt (LM) method. Stuxnet virus spread through removable storage media that used to transfer of data and virus to device connected to SCADA networks with ability to exploit the whole system. The reference dataset of mathematical model of Stuxnet virus dynamics is generated by the competency of Adams method and used arbitrary for training, testing and validation of BNCH through NNs learning with LM scheme. Comparative study of BNCH with reference results shows the matching of 4–7 decimal places of accuracy and the further validated through mean squared error-based figure of merit, histograms, and regression measures.

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Authors and Affiliations

  1. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan, ROC

    Muhammad Asif Zahoor Raja

  2. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Hira Naz

  3. Department of Mathematics, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Muhammad Shoaib

  4. School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia

    Ammara Mehmood

  5. School of Electronics Engineering, Kyungpook National University, Daegu, South Korea

    Ammara Mehmood

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  1. Muhammad Asif Zahoor Raja
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  2. Hira Naz
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  4. Ammara Mehmood
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Correspondence to Ammara Mehmood.

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Raja, M.A.Z., Naz, H., Shoaib, M. et al. Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure. Neural Comput & Applic 34, 5771–5790 (2022). https://doi.org/10.1007/s00521-021-06721-0

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