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Cyber Resilience Using State Estimation Updates and Cyber-Attack Matrix Classification Techniques.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Cyber Resilience Using State Estimation Updates and Cyber-Attack Matrix Classification Techniques./
作者:	Hopkins, Stephen M.
面頁冊數:	1 online resource (161 pages)
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798802713754

Cyber Resilience Using State Estimation Updates and Cyber-Attack Matrix Classification Techniques.
Hopkins, Stephen M.

Cyber Resilience Using State Estimation Updates and Cyber-Attack Matrix Classification Techniques. - 1 online resource (161 pages)

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.S.)--The University of West Florida, 2022.

Includes bibliographical references

The electric grid is an essential infrastructure powering medical support, air conditioning, security, automation, and information. Natural and artificial bad actors threaten the electric grid's stability, operations, and reliability. Smart grid technology allows consumers to generate and use the power under their control. The smart grid also adds more attack vectors and potential defense mechanisms. The main objective of this thesis is to develop and evaluate methods for improving the cyber-resilience of a smart grid. The first research problem considered in this thesis relates to enabling the power grid measurement and control system towards mitigating cyber-incidents. First, a compensating control in the form of a weighted matrix is incorporated into the Weighted Least Squares (WLS) state estimation process. This Cyber Attack Matrix (CAM) adjusts detected false data injections (FDI) towards actual data values, thus eliminating the impact of an FDI on the electric grid. The research simulated an FDI, which shifted the power demands from the actual values. The CAM integrated WLS mitigated the FDI and produced the true values.The second research problem addressed in this thesis involves detecting FDI data in the measurements. One of the significant challenges presented by FDI in power measurements is that it can exist undetected in assumed good data. Yet, the FDI can influence or leverage decision-making on the data. This research investigated eleven established outlier algorithms and selected five to detect the FDI. The detected FDI is compensated out of the measurements using a factor inserted into the CAM. The research simulated an FDI detected successfully by the Chauvenet, Thompson, and Peirce outlier algorithms for small and large sample sizes. The Grubbs outlier algorithm successfully detected outliers when the sample size was large. The simulation also correctly identified FDI within a good measurement range yet exceeded a maximum range of the smart grid components.The third research problem addressed in this thesis investigates consumer-provided smart grid component data to verify and validate the FDI detections. This data, which can be sent outside primary measurement methods, raises the confidence of the FDI detections and CAM adjustments. The research reviewed four potential data verifications methods and demonstrated good performance of a simple linear regression method through simulation. The thesis proposed a state estimator using a Cyber Attack Matrix, outlier algorithms, and smart grid-enabled verification methods, which successfully mitigated FDI attacks. Further, it is also shown that these methods extend the range of available avoidance, transference, and mitigation opportunities for increased cyber-resilience state estimation in the presence of cyber-attacks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798802713754Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Cyber resilienceIndex Terms--Genre/Form:

554714
Electronic books.

Cyber Resilience Using State Estimation Updates and Cyber-Attack Matrix Classification Techniques.
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