TY - GEN
T1 - Real-Time Detection of Cyber-Attacks in Modern Power Grids with Uncertainty using Deep Learning
AU - Mohammadpourfard, Mostafa
AU - Ghanaatpishe, Fateme
AU - Weng, Yang
AU - Genc, Istemihan
AU - Sandikkaya, Mehmet Tahir
N1 - Funding Information:
This work was supported by TUBITAK and European Commission Horizon 2020 Marie Sklodowska-Curie Actions Cofund program (Project Number: 120C080)
Funding Information:
This work was supported by TÜB˙TAK and European Commission Horizon 2020 Marie Skłodowska-Curie Actions Cofund program (Project Number: 120C080).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The smart grid, which is critical for developing smart cities, has a tool called state estimation (SE), which enables operators to monitor the system's stability. While the SE result is significant for future control operations, its reliability is strongly dependent on the data integrity of the information obtained from the dispersed measuring devices. However, the dependence on communication technology renders smart grids vulnerable to advanced data integrity attacks, presenting significant concerns to the overall reliability of SE. Among these attacks, the false data-injection attack (FDIA) is gaining popularity owing to its potential to disrupt network operations without being discovered by bad data detection (BDD) methods. Existing countermeasures are limited in their ability to cope with sudden physical changes in the smart grid, such as line outages, due to their development for a certain system specifications. Therefore, the purpose of this paper is to develop an attack detection scheme to find cyber-attacks in smart grids that are influenced by contingencies. In particular, a detection framework based on long short-term memory (LSTM) is proposed to discern electrical topology change in smart grids from real-time FDIAs. Results show that the developed framework surpasses the present techniques.
AB - The smart grid, which is critical for developing smart cities, has a tool called state estimation (SE), which enables operators to monitor the system's stability. While the SE result is significant for future control operations, its reliability is strongly dependent on the data integrity of the information obtained from the dispersed measuring devices. However, the dependence on communication technology renders smart grids vulnerable to advanced data integrity attacks, presenting significant concerns to the overall reliability of SE. Among these attacks, the false data-injection attack (FDIA) is gaining popularity owing to its potential to disrupt network operations without being discovered by bad data detection (BDD) methods. Existing countermeasures are limited in their ability to cope with sudden physical changes in the smart grid, such as line outages, due to their development for a certain system specifications. Therefore, the purpose of this paper is to develop an attack detection scheme to find cyber-attacks in smart grids that are influenced by contingencies. In particular, a detection framework based on long short-term memory (LSTM) is proposed to discern electrical topology change in smart grids from real-time FDIAs. Results show that the developed framework surpasses the present techniques.
KW - Cybersecurity
KW - deep learning
KW - smart grid
KW - topology changes
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U2 - 10.1109/SEST53650.2022.9898413
DO - 10.1109/SEST53650.2022.9898413
M3 - Conference contribution
AN - SCOPUS:85140822575
T3 - SEST 2022 - 5th International Conference on Smart Energy Systems and Technologies
BT - SEST 2022 - 5th International Conference on Smart Energy Systems and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Smart Energy Systems and Technologies, SEST 2022
Y2 - 5 September 2022 through 7 September 2022
ER -