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FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries.

Priyanka Verma1, John G Breslin1, Donna O'Shea2

  • 1Data Science Institute, University of Galway, H91TK33 Galway, Ireland.

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Summary

This study introduces a Federated Learning enabled Deep Intrusion Detection (FLDID) framework to combat cyber threats in smart manufacturing. FLDID enhances threat detection by enabling collaborative modeling across industries, overcoming limited attack data challenges.

Keywords:
IIoTIndustry 4.0cyber threatsdeep learningfederated learningintrusion detectionsmart manufacturing

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Area of Science:

  • Cybersecurity
  • Industrial Internet of Things (IIoT)
  • Industry 4.0

Background:

  • Industry 4.0, driven by IIoT, offers increased efficiency and cost reduction in manufacturing.
  • IIoT adoption introduces significant cyber threats to smart industries.
  • Detecting cyber threats in complex, heterogeneous smart manufacturing environments is challenging due to insufficient attack data.

Purpose of the Study:

  • To propose a Federated Learning enabled Deep Intrusion Detection (FLDID) framework for enhanced cyber threat detection in smart manufacturing.
  • To address the challenge of limited attack traces in individual smart industries by enabling collaborative model building.
  • To ensure data privacy during collaborative model training using Paillier-based encryption.

Main Methods:

  • Development of a Federated Learning enabled Deep Intrusion Detection (FLDID) framework.
  • Implementation of a hybrid deep learning model combining Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) for intrusion detection.
  • Utilization of Paillier-based encryption for secure communication between edge devices and the server to protect model gradients.

Main Results:

  • The FLDID framework effectively detects cyber threats in smart manufacturing environments.
  • Collaborative modeling overcomes the limitation of insufficient attack examples faced by individual industries.
  • Experimental results demonstrate superior performance compared to state-of-the-art approaches on a public dataset.

Conclusions:

  • The proposed FLDID framework offers a robust and privacy-preserving solution for cyber threat detection in Industry 4.0.
  • Federated learning is a viable approach to enhance cybersecurity in interconnected industrial systems.
  • The hybrid deep learning model effectively identifies intrusions in complex smart manufacturing networks.