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Physical-Model-Checking to Detect Switching-Related Attacks in Power Systems.

Mohamad El Hariri1, Samy Faddel2, Osama Mohammed3

  • 1Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA. melha003@fiu.edu.

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This study introduces an AI-powered intrusion detection system (IDS) to safeguard power grids against sophisticated cyberattacks. The intelligent IDS effectively identifies malicious control commands, enhancing power system security.

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agent systemscyber-physical securitydecentralized controlintelligent systems

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Conventional intrusion detection systems (IDS) struggle against advanced cyber threats targeting power grids.
  • Sophisticated attackers can evade rule-based IDS, posing significant risks to critical infrastructure.

Purpose of the Study:

  • To develop an AI-driven intrusion detection framework for power grid control systems.
  • To detect tampered control commands that threaten power system stability.

Main Methods:

  • Utilizing artificial intelligence (AI) to learn power system load flow characteristics.
  • Employing an expert system to validate control commands against physical operational constraints.
  • Analyzing network packet contents for malicious or erroneous data.

Main Results:

  • The AI framework accurately identifies malicious and erroneous control commands.
  • Simulations on a 14-bus IEEE benchmark system demonstrated high detection accuracy.
  • Experimental verification on a small power system with IEC 61850 architecture confirmed effectiveness.

Conclusions:

  • The proposed AI-based intrusion detection framework enhances power grid security.
  • This intelligent system offers a robust defense against cyberattacks targeting control commands.
  • The framework ensures power systems remain in a secure operational state.