2026-09-19T11:20:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1270252026-06-16T18:52:54Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Marija Ilić.
Jevtić, Ana,Ph. D.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-09-03T17:42:40Z
2020-09-03T17:42:40Z
2020
2020
https://hdl.handle.net/1721.1/127025
1191625291
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 99-108).
Many critical infrastructures, such as transportation and electric energy networks, and health care, are now becoming highly integrated with information and communication technology, in order to be more efficient and reliable. These cyber-physical systems (CPS) now face an increasing threat of cyber-attacks. Intelligent attackers can leverage their knowledge of the system, disruption, and disclosure resources to critically damage the system while remaining undiscovered. In this dissertation, we develop a defense strategy, with the ability to uncover malicious and intelligent attacks and enable resilient operation of cyber-physical systems. Specifically, we apply this defense strategy to power systems, described by linear frequency dynamics around the nominal operating point. Our methodology is based on the notion of data aggregation as a tool for extracting internal information about the system that may be unknown to the attacker. As the first step to resilience and security, we propose several methods for active attack detection in cyber-physical systems. In one approach we design a clustering-based moving-target active detection algorithm and evaluate it against stealthy attacks on the 5-bus and 24-bus power grids. Next, we consider an approach based on Interaction Variables (IntVar), as another intuitive way to extract internal information in power grids. We evaluate the eectiveness of this approach on Automatic Generation Control (AGC), a vital control mechanism in today's power grid. After an attack has been detected, mitigation procedures must be put in place to allow continued reliable operation or graceful degradation of the power grid. To that end, we develop a resilient state estimation algorithm, that provides the system operator with situational awareness in the presence of wide-spread coordinated cyber-attacks when many system measurements may become unavailable.
by Ana Jevtić.
Ph.D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
108 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Cyber-attack detection and resilient state estimation in power systems
Thesis
application/pdf
2020-09-03T17:42:39Z
Publication
Doctoral
EECS
unknown
unknown
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<Abstract>Many critical infrastructures, such as transportation and electric energy networks, and health care, are now becoming highly integrated with information and communication technology, in order to be more efficient and reliable. These cyber-physical systems (CPS) now face an increasing threat of cyber-attacks. Intelligent attackers can leverage their knowledge of the system, disruption, and disclosure resources to critically damage the system while remaining undiscovered. In this dissertation, we develop a defense strategy, with the ability to uncover malicious and intelligent attacks and enable resilient operation of cyber-physical systems. Specifically, we apply this defense strategy to power systems, described by linear frequency dynamics around the nominal operating point. Our methodology is based on the notion of data aggregation as a tool for extracting internal information about the system that may be unknown to the attacker. As the first step to resilience and security, we propose several methods for active attack detection in cyber-physical systems. In one approach we design a clustering-based moving-target active detection algorithm and evaluate it against stealthy attacks on the 5-bus and 24-bus power grids. Next, we consider an approach based on Interaction Variables (IntVar), as another intuitive way to extract internal information in power grids. We evaluate the eectiveness of this approach on Automatic Generation Control (AGC), a vital control mechanism in today's power grid. After an attack has been detected, mitigation procedures must be put in place to allow continued reliable operation or graceful degradation of the power grid. To that end, we develop a resilient state estimation algorithm, that provides the system operator with situational awareness in the presence of wide-spread coordinated cyber-attacks when many system measurements may become unavailable.</Abstract>
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