LAPSE:2023.11251v1
Published Article

LAPSE:2023.11251v1
Random-Enabled Hidden Moving Target Defense against False Data Injection Alert Attackers
February 27, 2023
Abstract
Hidden moving target defense (HMTD) is a proactive defense strategy that is kept hidden from attackers by changing the reactance of transmission lines to thwart false data injection (FDI) attacks. However, alert attackers with strong capabilities pose additional risks to the HMTD and thus, it is much-needed to evaluate the hiddenness of the HMTD. This paper first summarizes two existing alert attacker models, i.e., bad-data-detection-based alert attackers and data-driven alert attackers. Furthermore, this paper proposes a novel model-based alert attacker model that uses the MTD operation models to estimate the dispatched line reactance. The proposed attacker model can use the estimated line reactance to construct stealthy FDI attacks against HMTD methods that lack randomness. We propose a novel random-enabled HMTD (RHMTD) operation method, which utilizes random weights to introduce randomness and uses the derived hiddenness operation conditions as constraints. RHMTD is theoretically proven to be kept hidden from three alert attacker models. In addition, we analyze the detection effectiveness of the RHMTD against three alert attacker models. Simulation results on the IEEE 14-bus systems show that traditional HMTD methods fail to detect attacks by the model-based alert attacker, and RHMTD is kept hidden from three alert attackers and is effective in detecting attacks by three alert attackers.
Hidden moving target defense (HMTD) is a proactive defense strategy that is kept hidden from attackers by changing the reactance of transmission lines to thwart false data injection (FDI) attacks. However, alert attackers with strong capabilities pose additional risks to the HMTD and thus, it is much-needed to evaluate the hiddenness of the HMTD. This paper first summarizes two existing alert attacker models, i.e., bad-data-detection-based alert attackers and data-driven alert attackers. Furthermore, this paper proposes a novel model-based alert attacker model that uses the MTD operation models to estimate the dispatched line reactance. The proposed attacker model can use the estimated line reactance to construct stealthy FDI attacks against HMTD methods that lack randomness. We propose a novel random-enabled HMTD (RHMTD) operation method, which utilizes random weights to introduce randomness and uses the derived hiddenness operation conditions as constraints. RHMTD is theoretically proven to be kept hidden from three alert attacker models. In addition, we analyze the detection effectiveness of the RHMTD against three alert attacker models. Simulation results on the IEEE 14-bus systems show that traditional HMTD methods fail to detect attacks by the model-based alert attacker, and RHMTD is kept hidden from three alert attackers and is effective in detecting attacks by three alert attackers.
Record ID
Keywords
alert attacker model, D-FACTS device, false data injection attack, hidden moving target defense, state estimation, unsupervised learning
Subject
Suggested Citation
Liu B, Wu H, Yang Q, Zhang H. Random-Enabled Hidden Moving Target Defense against False Data Injection Alert Attackers. (2023). LAPSE:2023.11251v1
Author Affiliations
Liu B: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA [ORCID]
Wu H: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA [ORCID]
Yang Q: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA [ORCID]
Zhang H: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
Wu H: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA [ORCID]
Yang Q: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA [ORCID]
Zhang H: Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
Journal Name
Processes
Volume
11
Issue
2
First Page
348
Year
2023
Publication Date
2023-01-21
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr11020348, Publication Type: Journal Article
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LAPSE:2023.11251v1
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https://doi.org/10.3390/pr11020348
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[v1] (Original Submission)
Feb 27, 2023
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Feb 27, 2023
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