Vol. 4 No. 2 (2024): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

A SHORT REVIEW: CYBER ATTACKS AND DETECTION METHODS BASED ON MACHINE LEARNING AND DEEP LEARNING APPROACHES IN SMART GRIDS

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  • Mehmet Karayel,
  • Nevcihan DURU,
  • Mehmet KARA
Mehmet Karayel
Kocaeli Üniversity
Actual journals and last information about wireless communication

Published 31.07.2024

Keywords

  • Power Systems,
  • Smart Grids,
  • Cyber Attack,
  • False Data Injection Attack,
  • FDIA,
  • Machine Learning,
  • Deep Learning,
  • CNN,
  • RNN,
  • LSTM
    • ...More
    Less

Copyright (c) 2024 Mehmet Karayel; Nevcihan DURU, Mehmet KARA (Co-Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Power systems and smart grids constitute critical instruments of national security and the economy. In case of the power system malfunctioning, millions of people are affected. Furthermore, there are extreme financial losses, irreversible data casualties and service outages. Recently, the use of commercial smart measuring and control devices in the field of electricity and power systems has become widespread due to the development of applicable technologies and the reduction of the costs of devices. Although this situation has increased traceability and manageability, it also made smart grids more vulnerable to cyber threats compared to the traditional power systems used before. Cyber threats in smart grids are generally categorized as eavesdropping the data to possess detailed information about the system, tampering with data to disturb the system's stability, denial of services to block accessibility and injecting malicious software that can cause damage to the system. FDI attack is considered one of the most severe cyber-attack types due to its stealthy. FDI attacks disrupt the entire stabilization of the smart grid gradually. Machine learning and deep learning methods in supervised, semi-supervised and unsupervised domains have been widely used to protect smart grids against cyber threats by assisting conventional bad data detection mechanisms. Successful results have mainly been obtained by deep learning algorithms such as CNN and RNN. These algorithms have been supported with improved feature selection techniques to increase the accuracy of the detection and decrease the computational burden of the models. The purpose of the paper is to briefly summarize and combine the significance of smart grids, vulnerabilities of smart grids, cyber threats to smart grids, deep learning and machine learning methods applied against cyber-attacks, especially FDI attacks considered to be the most dangerous attack type and potential future research areas.

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