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Structural Deep Learning for Guarant...

Arizona State University., Electrical Engineering.

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Structural Deep Learning for Guaranteed Attacks with Zero System Knowledge /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Structural Deep Learning for Guaranteed Attacks with Zero System Knowledge // Napoleon Costilla-Enriquez.
Author:	Costilla-Enriquez, Napoleon,
Description:	1 electronic resource (133 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30810936
ISBN:	9798381157512

Structural Deep Learning for Guaranteed Attacks with Zero System Knowledge /
Costilla-Enriquez, Napoleon,

Structural Deep Learning for Guaranteed Attacks with Zero System Knowledge /Napoleon Costilla-Enriquez. - 1 electronic resource (133 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

The present outlook in power systems is rapidly changing due to the introduction of (1) new active devices into the grid, such as photovoltaic (PV) panels, wind generators, and energy storage devices, and (2) new data from sensing and control devices. While this abundant data improves situational awareness and enhances control schemes, it can make the power grid more vulnerable than ever to cyber-attacks with dire consequences. Cyberattack withdraws much attention due to its potential impact, its financial losses, and its implications for national security. To understand the risks, this work looks into the operation of the electric grids, e.g., how to solve power flow equations. Specifically, this work investigates the good and the bad parts of existing methods and proposes to have a stochastic solution for power flow analysis for robustness. The finding is that no matter how the solution method is improved, system information is crucial to securely analyzing the grid. This gives utilities a false sense of security by hiding such information. For example, in a false data injection attack (FDIA), an attacker must know system information and measurements. If system information is hidden, the grid seems impossible to attack successfully, e.g., passing the Chi-square test based on system information. This dissertation shows that a carefully designed system can not only attack successfully but also with a strong performance guarantee.

English

ISBN: 9798381157512Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Deep learning

Structural Deep Learning for Guaranteed Attacks with Zero System Knowledge /
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520 $a The present outlook in power systems is rapidly changing due to the introduction of (1) new active devices into the grid, such as photovoltaic (PV) panels, wind generators, and energy storage devices, and (2) new data from sensing and control devices. While this abundant data improves situational awareness and enhances control schemes, it can make the power grid more vulnerable than ever to cyber-attacks with dire consequences. Cyberattack withdraws much attention due to its potential impact, its financial losses, and its implications for national security. To understand the risks, this work looks into the operation of the electric grids, e.g., how to solve power flow equations. Specifically, this work investigates the good and the bad parts of existing methods and proposes to have a stochastic solution for power flow analysis for robustness. The finding is that no matter how the solution method is improved, system information is crucial to securely analyzing the grid. This gives utilities a false sense of security by hiding such information. For example, in a false data injection attack (FDIA), an attacker must know system information and measurements. If system information is hidden, the grid seems impossible to attack successfully, e.g., passing the Chi-square test based on system information. This dissertation shows that a carefully designed system can not only attack successfully but also with a strong performance guarantee.
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650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
650 4 $a Alternative energy. $3 3436775
653 $a Deep learning
653 $a Zero system knowledge
653 $a Photovoltaic panels
653 $a Energy storage devices
653 $a Chi-square test
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690 $a 0984
690 $a 0363
710 2 $a Arizona State University. $b Electrical Engineering. $e degree granting institution. $3 3771820
720 1 $a Weng, Yang $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0010
791 $a Ph.D.
792 $a 2023
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