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Gradient scaling and segmented SoftMax Regression Federated Learning (GDS-SRFFL): a novel methodology for attack detection in industrial internet of things (IIoT) networks

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Abstract

Industrial internet of things (IIoT) is considered as large-scale IoT-based network comprising of sensors, communication channels, and security protocols used in Industry 4.0 for diverse real-time operations. Industrial IoT (IIoT) networks are vulnerable to diverse cyber threats and attacks. Attack detection is the biggest security issue in the IIoT. Various traditional attack detection methods are proposed by several researchers but all are insufficient to protect privacy and security. To address the issue, a novel Gradient Descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) method is introduced for IIoT network attack detection. The aim of the GDS-SRFFL method is to enhance the security of an IIoT network. Initially, the novelty of Gradient Descent Scaling-based preprocessing is applied to the raw dataset for obtaining feature feature-scaled preprocessed network sample. Then, the unwanted intrusions are discovered by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to ensure the protection of IoT networks with the novelty of SoftMax Regression. In order to validate the proposed methodology, experimentations were conducted on different parameters, namely accuracy, precision, recall, specificity, and attack detection time, and the results concluded that proposed GDS-SRFFL has improved accuracy by 10%, precision by 13%, recall by 10%, specificity by 11% as well as minimum attack detection time by 28% as compared to existing techniques like CNN + LSTM (Altunay and Albayrak in Eng Sci Technol Int J 38:101322, 2023, https://doi.org/10.1016/j.jestch.2022.101322), Enhanced Deep and Ensemble learning in SCADA-based IIoT network (Khan et al. in IEEE Trans Ind Inf 19(1):1030–1038, https://doi.org/10.1109/TII.2022.3190352), RNN (Ullah and Mahmoud in IEEE Access 10:62722–62750, 2022, https://doi.org/10.1109/ACCESS.2022.3176317), and other CNN methods. The proposed method “GDS-SRFFL” has overall accuracy of 89.42% as compared to other existing methods.

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Funding

The authors received no specific funding for this study.

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Authors and Affiliations

  1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India

    Vijay Anand Rajasekaran, Alagiri Indirajithu & P. Jayalakshmi

  2. Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam

    Anand Nayyar

  3. Shiv Nadar Institution of Eminence, Greater Noida, Uttar Pradesh, 201314, India

    Balamurugan Balusamy

Authors
  1. Vijay Anand Rajasekaran
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  2. Alagiri Indirajithu
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  3. P. Jayalakshmi
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  4. Anand Nayyar
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  5. Balamurugan Balusamy
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Contributions

Author Contributions: Idea Conceptualization: Vijay Anand R, Alagiri I, Jayalakshmi P, Anand Nayyar, Balamurugan Balusamy, Writing and Drafting: Vijay Anand R, Alagiri I, Jayalakshmi P, Anand Nayyar, Balamurugan Balusamy; Editing and Language Checks: Anand Nayyar, Jayalakshmi P; Experimentation: Vijay Anand R, Alagiri I, Jayalakshmi P, Balamurugan Balusamy; Proofing: Anand Nayyar, Balamurugan Baluswamy;

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Correspondence to Anand Nayyar.

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Rajasekaran, V.A., Indirajithu, A., Jayalakshmi, P. et al. Gradient scaling and segmented SoftMax Regression Federated Learning (GDS-SRFFL): a novel methodology for attack detection in industrial internet of things (IIoT) networks. J Supercomput 80, 16860–16886 (2024). https://doi.org/10.1007/s11227-024-06109-6

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