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Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition.

Faheed A F Alrslani1, Manal Abdullah Alohali2, Mohammed Aljebreen3

  • 1Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia.

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|January 31, 2025
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Summary
This summary is machine-generated.

This study introduces a novel Attribute Reduction with Deep Learning-based False Data Injection Attack Recognition (ARDL-FDIAR) technique to enhance power system security. The ARDL-FDIAR method effectively detects and mitigates false data injection attacks, improving grid resilience.

Keywords:
Cetacean optimization AlgorithmCyberattackDeep Belief NetworkDeep learningFalse data injection attack

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • False Data Injection Attacks (FDIA) pose a significant threat to power system operations by bypassing conventional security measures.
  • The increasing integration of renewables necessitates real-time monitoring and robust security for grid stability.
  • State estimation algorithms, crucial for grid operation, are vulnerable to malicious data manipulation during FDIA.

Purpose of the Study:

  • To introduce a novel technique for recognizing False Data Injection Attacks (FDIA) in power systems.
  • To enhance the security and resilience of power grids against sophisticated cyber threats.
  • To improve the accuracy and efficiency of FDIA detection through advanced deep learning methods.

Main Methods:

  • Developed an Attribute Reduction with Deep Learning-based False Data Injection Attack Recognition (ARDL-FDIAR) technique.
  • Utilized Z-score normalization for data scaling and the modified Lemrus optimization algorithm (MLOA) for feature selection.
  • Employed an improved deep belief network (IDBN) model for FDIA detection, with hyperparameters tuned by the Cetacean Optimization Algorithm (COA).

Main Results:

  • The ARDL-FDIAR technique demonstrated enhanced security performance in detecting FDIA.
  • Experimental results confirmed the effectiveness of the proposed method compared to existing deep learning approaches.
  • The study validated the improved accuracy and reliability of the IDBN model with COA-based hyperparameter tuning.

Conclusions:

  • The ARDL-FDIAR technique offers a robust solution for real-time FDIA detection in power systems.
  • The proposed method significantly improves grid security and operational resilience.
  • This research contributes to securing smart grids against emerging cyber threats.