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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.
Sensors (Basel, Switzerland)
|August 2, 2018
Summary
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.
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.
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