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
Dans un réseau électrique, il est important de détecter une cyberattaque. Dans cet article, nous proposons une méthode pour détecter les attaques par fausse injection de données (FDI) dans l'estimation d'état distribuée. Une attaque FDI est bien connue comme l'une des cyberattaques typiques dans un réseau électrique. En tant que méthode de détection d'attaques FDI, nous envisageons de calculer le résidu (c'est-à-dire la différence entre les valeurs observées et estimées). Dans la méthode de détection proposée, le résidu provisoire (erreur estimée) dans ADMM (Alternating Direction Method of Multipliers), qui est l'une des méthodes puissantes d'optimisation distribuée, est appliqué. Tout d'abord, l'effet d'une attaque FDI est analysé. Ensuite, sur la base du résultat de l'analyse, un paramètre de détection est introduit sur la base du résidu. Une méthode de détection utilisant ce paramètre est alors proposée. Enfin, la méthode proposée est démontrée à travers un exemple numérique sur le système 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},}
Copier
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 -