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Zhang, Y., Wang, J. and Chen, B. (2021) Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach. IEEE Transactions on Smart Grid, 12, 623-634.
https://doi.org/10.1109/TSG.2020.3010510

has been cited by the following article:

• TITLE: Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

  AUTHORS: Tala Talaei Khoei, Hadjar Ould Slimane, Naima Kaabouch

  KEYWORDS: Smart Grid, Architecture, Cyber-Attacks, Network Security, Confidentiality, Integrity, Availability, Accountability, Countermeasures, Detection Techniques

  JOURNAL NAME: Communications and Network, Vol.14 No.4, November 24, 2022

  ABSTRACT: One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.