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A lightweight optimized intrusion detection system using machine learning for edge-based IIoT security

Author

Listed:
  • Ravi Shekhar Tiwari

    (Mahindra University)

  • D. Lakshmi

    (VIT Bhopal University)

  • Tapan Kumar Das

    (Vellore Institute of Technology)

  • Asis Kumar Tripathy

    (Vellore Institute of Technology)

  • Kuan-Ching Li

    (Providence University)

Abstract

The Industrial Internet of Things (IIoT) attributes to intelligent sensors and actuators for better manufacturing and industrial operations. At the same time, IIoT devices must be secured from the potentially catastrophic effects of eventual attacks, and this necessitates real-time prediction and preventive strategies for cyber-attack vectors. Due to this, the objective of this investigation is to obtain a high-accuracy intrusion detection technique with a minimum payload. As the experimental process, we have utilized the IIoT network security dataset, namely WUSTL-IIOT-2021. The feature selection technique Particle Swarm Optimization (PSO) and feature reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are applied. Additionally, the Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS) are used to detect payloads that can interfere with the normal operation of an application. Both PSO and PCA combined with MARS have produced predictive results with an exceptional accuracy of 100%. Yet, the trained Machine Learning (ML) model is quantized with 4-bit and 8-bit, and it is deployed on Azure IoT Edge to simulate edge devices. Experimental results show that the latency of the model was reduced by 25% on quantization.

Suggested Citation

  • Ravi Shekhar Tiwari & D. Lakshmi & Tapan Kumar Das & Asis Kumar Tripathy & Kuan-Ching Li, 2024. "A lightweight optimized intrusion detection system using machine learning for edge-based IIoT security," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(3), pages 605-624, November.
  • Handle: RePEc:spr:telsys:v:87:y:2024:i:3:d:10.1007_s11235-024-01200-y
    DOI: 10.1007/s11235-024-01200-y
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    References listed on IDEAS

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    1. Dezhi Han & HongXu Zhou & Tien-Hsiung Weng & Zhongdai Wu & Bing Han & Kuan-Ching Li & Al-Sakib Khan Pathan, 2023. "LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 84(4), pages 549-564, December.
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    Cited by:

    1. Ravi Shekhar Tiwari & Tapan Kumar Das & Asis Kumar Tripathy & Kuan-Ching Li, 2025. "Gait identification based on deepwalk features using CNN and LSTM: an advanced biometric approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(3), pages 1-16, September.

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