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Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment.

Yi Chen1,2,3, Kadhim Hayawi4, Qian Zhao1

  • 1College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

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|September 23, 2022
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Summary
This summary is machine-generated.

This study introduces a novel Vector Auto-Regression (VAR) method for detecting false data injection attacks (FDIA) in smart grids. The VAR-based approach enhances smart grid security and reliability by improving malicious attack detection capabilities.

Keywords:
attack detectionfalse data injection attack (FDIA)smart gridvector auto-regression (VAR)

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Smart grids face significant security threats from false data injection attacks (FDIA) due to advanced communication technologies.
  • Effective malicious attack detection is crucial for maintaining the safe operation and reliable power supply of smart grids.

Purpose of the Study:

  • To propose and evaluate a novel method for FDIA detection in smart grids using a Vector Auto-Regression (VAR) model.
  • To enhance the security and reliability of smart grid applications through improved FDIA detection.

Main Methods:

  • A Vector Auto-Regression (VAR) model is employed for short-term prediction of FDIA.
  • Measurement residual analysis using infinite norm and 2-norm is integrated for classification detection.
  • The proposed method is implemented within an edge computing architecture.
  • Experiments were conducted using the IEEE 14-bus system power grid model.

Main Results:

  • The VAR-based FDIA detection method demonstrated superior performance compared to the auto-regressive (AR) model.
  • The proposed method effectively detects false data injection attacks in smart grid environments.
  • The integration of VAR with norm-based residual analysis proved effective for FDIA detection.

Conclusions:

  • The proposed VAR-based method offers a robust solution for FDIA detection in smart grids.
  • This approach contributes to improving the overall security and reliability of smart grid operations.
  • The study highlights the potential of VAR models in addressing cybersecurity challenges in power systems.