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A Modular AI-Driven Intrusion Detection System for Network Traffic Monitoring in Industry 4.0, Using Nvidia Morpheus

Beatrice-Nicoleta Chiriac1, Florin-Daniel Anton1, Anca-Daniela Ioniță1

  • 1Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Sciences, National University of Science and Technology Polithenica Bucharest, 313 Spl. Independenței, RO060042 Bucharest, Romania.

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Summary

This study introduces an AI-powered intrusion detection system (IDS) for Industry 4.0, achieving 90% accuracy using XGBoost and federated learning. It rapidly analyzes network traffic, enhancing cybersecurity defenses against evolving threats.

Keywords:
Industry 4.0Internet of Things (IoT)artificial intelligenceevent monitoringintrusion detection and protection systemnetworkingneural networks

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Industrial Control Systems

Background:

  • Traditional cybersecurity defenses are insufficient against the increasing volume and sophistication of daily attacks.
  • Industry 4.0 environments require advanced solutions to handle large data volumes and protect against cyber threats.

Purpose of the Study:

  • To develop a generic, AI-based network intrusion detection system (IDS) model tailored for Industry 4.0 applications.
  • To leverage the Nvidia Morpheus framework for enhanced computational performance in cybersecurity.

Main Methods:

  • Implementation of a modular IDS with two data analysis pipelines.
  • Utilizing a pre-trained XGBoost (eXtreme Gradient Boosting) model for classification.
  • Integration of federated learning for rapid analysis of over 500,000 inputs in approximately 10 seconds.
  • Employing a generative adversarial network (GAN) to generate polymorphic network traffic for improved model robustness.

Main Results:

  • The XGBoost model achieved an accuracy score of up to 90%.
  • The IDS demonstrated a high analysis rate, processing over 500,000 inputs in nearly 10 seconds.
  • The GAN integration enhanced the classification performance of the intrusion detection model.

Conclusions:

  • The proposed AI-driven IDS offers a robust and efficient solution for Industry 4.0 cybersecurity.
  • The integration of XGBoost, federated learning, and GANs significantly improves intrusion detection capabilities.
  • This approach addresses the critical need for advanced cybersecurity measures in industrial digital environments.