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Detection of False Data Injection Attacks in Smart Grids Based on Expectation Maximization.

Pengfei Hu1,2, Wengen Gao1,2, Yunfei Li1,2

  • 1School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China.

Sensors (Basel, Switzerland)
|February 11, 2023
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Summary

This study introduces a novel statistical learning algorithm to detect and locate false data injection attacks (FDIAs) in smart grids. The method effectively identifies manipulated data, enhancing smart grid security and state estimation accuracy.

Keywords:
attack detectionattack locationfalse data injection attackssmart gridstatistical learning methods

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Area of Science:

  • Electrical Engineering
  • Cybersecurity
  • Statistical Learning

Background:

  • Smart grid security relies on accurate state estimation, which is vulnerable to sophisticated false data injection attacks (FDIAs).
  • FDIAs manipulate measurement data, bypassing security systems and compromising grid state estimation accuracy and reliability.
  • Tampering with bus measurement data in smart grids introduces characteristic error offsets crucial for detection.

Purpose of the Study:

  • To develop and validate a novel attack-detection algorithm for identifying and classifying FDIAs in smart grids.
  • To enhance the accuracy and speed of state estimation by mitigating the impact of malicious data injection.
  • To pinpoint the specific buses targeted by attackers within the smart grid infrastructure.

Main Methods:

  • Proposed an attack-detection algorithm leveraging statistical learning principles to analyze measurement error characteristics.
  • Combined the k-means++ and Expectation-Maximization (EM) algorithms for robust error parameter estimation and false data classification.
  • Validated the algorithm's efficacy using standard test systems, including the IEEE 5-bus and IEEE 14-bus systems.

Main Results:

  • The proposed algorithm successfully detects and classifies false data injected into smart grid measurements.
  • Achieved a detection time of less than 0.011883 seconds, significantly improving response times.
  • Demonstrated high accuracy in locating tampered buses, with a success rate exceeding 95%.

Conclusions:

  • The integrated k-means++ and EM algorithm approach provides an effective solution for detecting and locating FDIAs in smart grids.
  • The method enhances the security and reliability of smart grid operations by ensuring the integrity of state estimation.
  • The algorithm's speed and accuracy offer a promising advancement in protecting critical energy infrastructure from cyber threats.