TY - JOUR
T1 - Revealing a New Vulnerability of Distributed State Estimation
T2 - A Data Integrity Attack and an Unsupervised Detection Algorithm
AU - Shefaei, Alireza
AU - Mohammadpourfard, Mostafa
AU - Mohammadi-Ivatloo, Behnam
AU - Weng, Yang
N1 - Funding Information:
This work was supported by the NSF under Grants 1810537 and NSF 2048288.
Publisher Copyright:
© 2014 IEEE.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - This article proposes a distributed false data injection attack (FDIA) by attacking to the boundary buses in an interconnected power system. The proposed attack utilizes the measurements corresponding to a set of boundary buses in each neighboring areas to inject arbitrary errors to the estimated states of those buses. It is demonstrated that the attack not only gets through the robust distributed estimators but also bypasses the convergence-based detection methods. Furthermore, in an illustrative example, the differences in the attack with the conventional FDIA are briefly explained. Then, finding the optimal attack vector to minimize the maximum difference between the per area errors by considering the attacker's limitations is formulated as a mixed-integer second-order cone programming problem. Finally, an unsupervised machine learning-based detection method is proposed utilizing a kernel density estimation technique along with statistical measures. This follows an outlier detection to filter out attacks. To show the performance of the detector, the n-1 contingency, which changes the probability distribution of data is analyzed. The proposed attack and detector are tested on various IEEE systems such as IEEE 14-bus and IEEE 118-bus test systems and the results are discussed.
AB - This article proposes a distributed false data injection attack (FDIA) by attacking to the boundary buses in an interconnected power system. The proposed attack utilizes the measurements corresponding to a set of boundary buses in each neighboring areas to inject arbitrary errors to the estimated states of those buses. It is demonstrated that the attack not only gets through the robust distributed estimators but also bypasses the convergence-based detection methods. Furthermore, in an illustrative example, the differences in the attack with the conventional FDIA are briefly explained. Then, finding the optimal attack vector to minimize the maximum difference between the per area errors by considering the attacker's limitations is formulated as a mixed-integer second-order cone programming problem. Finally, an unsupervised machine learning-based detection method is proposed utilizing a kernel density estimation technique along with statistical measures. This follows an outlier detection to filter out attacks. To show the performance of the detector, the n-1 contingency, which changes the probability distribution of data is analyzed. The proposed attack and detector are tested on various IEEE systems such as IEEE 14-bus and IEEE 118-bus test systems and the results are discussed.
KW - Cybersecurity
KW - decentralization
KW - distributed false data injection attack
KW - machine learning
KW - smart grid
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U2 - 10.1109/TCNS.2021.3091631
DO - 10.1109/TCNS.2021.3091631
M3 - Article
AN - SCOPUS:85112468120
VL - 9
SP - 706
EP - 718
JO - IEEE Transactions on Control of Network Systems
JF - IEEE Transactions on Control of Network Systems
SN - 2325-5870
IS - 2
ER -