IEICE TRANSACTIONS on Fundamentals
Detection of False Data Injection Attacks in Distributed State Estimation of Power Networks
Summary :
In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.5 pp.729-735
- Publication Date
- 2023/05/01
- Publicized
- 2022/10/24
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022MAP0010
- Type of Manuscript
- Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)
- Category
Authors
Sho OBATA
Hokkaido University
Koichi KOBAYASHI
Hokkaido University
Yuh YAMASHITA
Hokkaido University
Keyword
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Sho OBATA, Koichi KOBAYASHI, Yuh YAMASHITA, "Detection of False Data Injection Attacks in Distributed State Estimation of Power Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 5, pp. 729-735, May 2023, doi: 10.1587/transfun.2022MAP0010.
Abstract: In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022MAP0010/_p
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@ARTICLE{e106-a_5_729,
author={Sho OBATA, Koichi KOBAYASHI, Yuh YAMASHITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Detection of False Data Injection Attacks in Distributed State Estimation of Power Networks},
year={2023},
volume={E106-A},
number={5},
pages={729-735},
abstract={In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.},
keywords={},
doi={10.1587/transfun.2022MAP0010},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Detection of False Data Injection Attacks in Distributed State Estimation of Power Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 729
EP - 735
AU - Sho OBATA
AU - Koichi KOBAYASHI
AU - Yuh YAMASHITA
PY - 2023
DO - 10.1587/transfun.2022MAP0010
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2023
AB - In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.
ER -
English
