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This study introduces a novel protocol reverse engineering method for smart manufacturing network security. It enables AI-driven anomaly detection without expert input, enhancing security for complex industrial systems.

Keywords:
Anomaly detectionNetwork securitySmart manufacturing system

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Area of Science:

  • Industrial Control Systems Security
  • Artificial Intelligence in Cybersecurity
  • Network Anomaly Detection

Background:

  • Smart manufacturing systems, driven by the 4th Industrial Revolution, face increased network security challenges due to heightened connectivity.
  • Traditional network anomaly detection methods struggle with the dynamic and complex environments of smart manufacturing, requiring extensive manual effort to define normality.
  • Existing AI-based anomaly detection requires significant expert knowledge for feature extraction, limiting its applicability in diverse manufacturing settings.

Purpose of the Study:

  • To propose a protocol reverse engineering method to streamline the preprocessing stage for AI-based anomaly detection in smart manufacturing.
  • To enable AI models to learn network normality directly from collected data, eliminating the need for site-specific expert knowledge.
  • To address anomaly detection in less-studied non-polling or reporting network environments within manufacturing security.

Main Methods:

  • Developed a protocol reverse engineering technique to automatically extract features from network data.
  • Integrated this method into the preprocessing pipeline for AI-based anomaly detection systems.
  • Proposed a new anomaly detection approach utilizing external signatures, time information, interval patterns, and classified messages.

Main Results:

  • The proposed protocol reverse engineering method significantly reduces the reliance on expert knowledge for AI model training.
  • The new anomaly detection method demonstrates effectiveness in identifying anomalies within encrypted manufacturing protocols.
  • The approach is applicable to diverse smart manufacturing environments, including non-polling and reporting systems.

Conclusions:

  • Protocol reverse engineering offers a viable solution to overcome preprocessing limitations in AI-driven network anomaly detection for smart manufacturing.
  • The developed method enhances the efficiency and applicability of cybersecurity measures in evolving industrial control systems.
  • This research contributes to more robust and adaptable network security solutions for the 4th Industrial Revolution in manufacturing.