The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Em uma rede de energia, é importante detectar um ataque cibernético. Neste artigo, propomos um método para detectar ataques de injeção de dados falsos (FDI) na estimativa de estado distribuído. Um ataque FDI é bem conhecido como um dos ataques cibernéticos típicos em uma rede de energia. Como método de detecção de ataque FDI, consideramos o cálculo do residual (ou seja, a diferença entre os valores observados e estimados). No método de detecção proposto, é aplicado o resíduo provisório (erro estimado) em ADMM (Alternating Direction Method of Multipliers), que é um dos métodos poderosos em otimização distribuída. Primeiro, o efeito de um ataque FDI é analisado. Em seguida, com base no resultado da análise, é introduzido um parâmetro de detecção com base no resíduo. Um método de detecção usando este parâmetro é então proposto. Finalmente, o método proposto é demonstrado através de um exemplo numérico no sistema IEEE 14-bus.
Sho OBATA
Hokkaido University
Koichi KOBAYASHI
Hokkaido University
Yuh YAMASHITA
Hokkaido University
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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},}
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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 -