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Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures.

Wei Jiang1

  • 1Zhengzhou College of Finance and Economics, Zhengzhou 450000, China.

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This study introduces a novel machine learning system to detect voltage glitch attacks (VGAs) in IoT/IIoIIoT systems. The method uses semisupervised learning to identify and mitigate these security threats effectively.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Modern IoT/IIoT systems depend on cryptography for security.
  • Hardware attacks like voltage glitch attacks (VGAs) threaten system integrity and data confidentiality.
  • VGAs can bypass security features or extract sensitive information by manipulating system voltage.

Purpose of the Study:

  • To propose an innovative detection system for voltage glitch attacks (VGAs).
  • To enhance the security of IoT/IIoT digital systems against hardware manipulation.
  • To develop a robust defense mechanism using advanced machine learning techniques.

Main Methods:

  • Implementation of a semisupervised learning methodology.
  • Utilizing a hybrid combination of machine learning algorithms.
  • Employing a heuristic clustering method with linear fragmentation for group classes.
  • Leveraging Extreme Learning Machine (ELM) for hidden variable retrieval via convex optimization.

Main Results:

  • Successful development of a novel VGA detection system.
  • Demonstrated effectiveness of the semisupervised learning approach in identifying voltage manipulation.
  • The hybrid algorithm combination proved adept at analyzing system behavior under attack.

Conclusions:

  • The proposed machine learning system offers a promising solution for detecting VGAs in IoT/IIoT environments.
  • Semisupervised learning, combined with heuristic clustering and ELM, provides a powerful framework for cybersecurity defense.
  • This research contributes to securing digital infrastructure against sophisticated hardware-based attacks.