{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78661"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78661","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Malicious data detection and localization in state estimation leveraging system losses","abstract":"In power systems, economic dispatch, contingency analysis, and the detection of faulty equipment rely on the output of the state estimator. Typically, state estimations are made based on the network topology information and the measurements from a set of sensors within the network. The state estimates must be accurate even with the presence of corrupted measurements. Traditional techniques used to detect and identify bad sensor measurements in state estimation cannot thwart malicious sensor measurement modifications, such as malicious data injection attacks. Recent work by Niemira (2013) has compared real and reactive injection and flow measurements as indicators of attacks. In this work, we improve upon the method used in that work to further enhance the detectability of malicious data injection attacks, and to incorporate PMU measurements to detect and locate previously undetectable attacks.","abstract_html":"In power systems, economic dispatch, contingency analysis, and the detection of faulty equipment rely on the output of the state estimator. Typically, state estimations are made based on the network topology information and the measurements from a set of sensors within the network. The state estimates must be accurate even with the presence of corrupted measurements. Traditional techniques used to detect and identify bad sensor measurements in state estimation cannot thwart malicious sensor measurement modifications, such as malicious data injection attacks. Recent work by Niemira (2013) has compared real and reactive injection and flow measurements as indicators of attacks. In this work, we improve upon the method used in that work to further enhance the detectability of malicious data injection attacks, and to incorporate PMU measurements to detect and locate previously undetectable attacks.","abstract_has_math":false,"creators":["Lu, Miao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:33:51Z","date_published":"2015-07-22T22:33:51Z","updated_at":"2026-07-22T22:26:12Z","subjects":["false data injection","phasor measurement units","state estimation","power system cyber security"],"languages":["en"],"rights":["Copyright 2015 Miao Lu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78661","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Lu, Miao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:33:51Z","2017-07-23T09:15:17Z","2015-05","2015-04-27","2015-5"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["false data injection","phasor measurement units","state estimation","power system cyber security"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Miao Lu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78661"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In power systems, economic dispatch, contingency analysis, and the detection of faulty equipment rely on the output of the state estimator. Typically, state estimations are made based on the network topology information and the measurements from a set of sensors within the network. The state estimates must be accurate even with the presence of corrupted measurements. Traditional techniques used to detect and identify bad sensor measurements in state estimation cannot thwart malicious sensor measurement modifications, such as malicious data injection attacks. Recent work by Niemira (2013) has compared real and reactive injection and flow measurements as indicators of attacks. In this work, we improve upon the method used in that work to further enhance the detectability of malicious data injection attacks, and to incorporate PMU measurements to detect and locate previously undetectable attacks.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01","The student, Miao Lu, accepted the attached license on 2015-04-23 at 12:01.","The student, Miao Lu, submitted this Thesis for approval on 2015-04-23 at 12:11.","This Thesis was approved for publication on 2015-04-27 at 16:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8056 on 2015-07-22 at 14:18:44","Made available in DSpace on 2015-07-22T22:33:51Z (GMT). No. of bitstreams: 2 LU-THESIS-2015.pdf: 684550 bytes, checksum: 610e4b1f23239f5bf9033dffa98f3740 (MD5) LICENSE.txt: 4204 bytes, checksum: ac80478c72cb90d092368cf426d96453 (MD5) Previous issue date: 2015-04-27","Embargo set by: Seth Robbins for item 79902 Lift date: 2017-07-22T22:34:16Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 79902 on 2017-07-23T09:15:17Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Malicious data detection and localization in state estimation leveraging system losses"]}]}],"canonical_facts":{"dc:creator":["Lu, Miao"],"dc:date":["2015-07-22T22:33:51Z","2017-07-23T09:15:17Z","2015-05","2015-04-27","2015-5"],"dc:description":["In power systems, economic dispatch, contingency analysis, and the detection of faulty equipment rely on the output of the state estimator. Typically, state estimations are made based on the network topology information and the measurements from a set of sensors within the network. The state estimates must be accurate even with the presence of corrupted measurements. Traditional techniques used to detect and identify bad sensor measurements in state estimation cannot thwart malicious sensor measurement modifications, such as malicious data injection attacks. Recent work by Niemira (2013) has compared real and reactive injection and flow measurements as indicators of attacks. In this work, we improve upon the method used in that work to further enhance the detectability of malicious data injection attacks, and to incorporate PMU measurements to detect and locate previously undetectable attacks.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01","The student, Miao Lu, accepted the attached license on 2015-04-23 at 12:01.","The student, Miao Lu, submitted this Thesis for approval on 2015-04-23 at 12:11.","This Thesis was approved for publication on 2015-04-27 at 16:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8056 on 2015-07-22 at 14:18:44","Made available in DSpace on 2015-07-22T22:33:51Z (GMT). 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